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BIOLOGY DEPARTMENT
STUDENT SURVIVAL GUIDE
Fall 2006
Compiled by the Members of
The Biology Department at
The University of the South
Fall 2006
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Table of Contents
CHAPTER 1: LAB POLICIES ----------------------------------------------------------------------3
CHAPTER 2: USING YOUR SEWANEE ANGELS ACCOUNT----------------------------6
CHAPTER 3: LABORATORY NOTEBOOKS--------------------------------------------------8
CHAPTER 4: RESEARCH PROPOSAL GUIDELINES ------------------------------------ 11
CHAPTER 5: A GUIDE TO WRITING SCIENTIFIC PAPERS -------------------------- 27
CHAPTER 6: LITERATURE CITED ----------------------------------------------------------- 33
CHAPTER 7: AN INTRODUCTION TO GRAPHS AND DATA SUMMARIES ------ 36
CHAPTER 8: AN INTRODUCTION TO MICROSOFT EXCEL 2000 ------------------ 45
CHAPTER 9: USING EXCEL TO PLOT DATA (BIOLOGY 132) ----------------------- 65
CHAPTER 10: INFERENTIAL STATISTICS OR "STATISTICAL TESTS" --------- 71
CHAPTER 11: USE OF OXFORD® DIGITAL DIAL PIPETS ---------------------------- 83
CHAPTER 12: SPECTROPHOTOMETRY---------------------------------------------------- 87
CHAPTER 13: OSMOMETRY ------------------------------------------------------------------- 89
CHAPTER 14: USING MICROSCOPES ------------------------------------------------------- 91
CHAPTER 15: USING FIRST SEARCH AND OTHER LIBRARY RESOURCES --- 93
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CHAPTER 1: LAB POLICIES
APPLICATION OF THE HONOR CODE TO BIOLOGY LABORATORY
Students are to pay scrupulous attention to the Honor Code when making entries in their
laboratory notebooks and writing proposals and papers. Make the following guidelines your
own:
9 All data and observations in my notebook are to the best of my knowledge accurate
reports. Fabrication and alteration of data are unauthorized.
9 Copying data from another student’s book is unauthorized unless we obtained the
data together. Reading any answers to questions or discussions in another student’s
book or allowing someone to read my book are unauthorized, whether or not we were
partners.
9 Calculations and discussions may be written outside the laboratory. These may be
discussed with others, but the actual calculations and discussions in my book must be
written by me. I must state clearly which of these were influenced or aided by another
student or instructor other than my own.
Unless your professor indicates otherwise by asking specifically, in writing, for group
write-ups and proposals, this rule also applies to written material such as proposals and
reports to be turned in and graded. These MUST be written individually. In the case of
proposals, discussion within a lab group of the experiments to be run is highly
recommended. Discussion facilitates understanding. However, proposals MUST be
written individually. If there is more than one idea in a group regarding what to do in a
second week of experimentation, individuals in a group may propose different
experiments. However, during the second in-lab session all members of a group will be
expected to run the same experiment.
With regard to lab reports, while all the members of a group will be reporting on the same
experiments and reporting the same data, these MUST be written individually. Again,
you are encouraged to discuss the meanings of results but you are not allowed to write or
use portions of each others' reports.
9 Entering data into the computer under any other than my name and ID number is
unauthorized.
In addition, proper citation of sources used in writing proposals and papers is a MUST.
Use proper citation methods and formats as noted in the Survival Guide, using ideas and
citing the source for these ideas but do not copy.
9 Never cut and paste from a web site into your paper
9 Never copy portions from any source without using quotations.
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EQUIPMENT
At times, students will check out equipment as it pertains to individual labs. Some
equipment such as digital cameras and associated accessories can cost in excess of $300. It is
the policy of the Biology Department that students will be held personally responsible for
returning the equipment in working order or providing for its replacement. In the case of a
group of students checking out equipment, all members of the group will be held equally
responsible.
LABORATORY SAFETY GUIDELINES
Protective Equipment
√ Wear appropriate Personal Protective Equipment (PPE) as instructed. This includes, but
is not limited to, the use of aprons, goggles and gloves. In general, use this equipment
whenever dealing with chemicals of particular hazard, or in large volumes or high
concentration; or when working with flames, or gases under pressure.
√ Do not wear PPE outside of the lab unless necessary (ie to the restroom or classroom).
Doing so exposes other people and places to contaminants that should remain in the lab
and vice verse exposing your experiment to outside contaminants. Keep this in mind
when you must wear PPE to transfer work to another lab for example.
√ Learn the location and operation of eyewash, safety shower, and fire extinguisher. Know
what to do or whom to contact in case of an emergency.
Personal Hygiene
√ While Class Dress is not required, lab dress has its own requirements. Appropriate lab
dress is as follows: Wear shoes that will cover and protect the whole foot, leather is best.
Clothing, made of natural fibers, should cover the entire body; shirts should cover the
entire torso. Long sleeves and long pants are best, however, sleeves should not be loose
and floppy. You will not be allowed into lab without appropriate clothing. This is for
your safety and the safety of those around you.
√ Wash hands frequently: before you begin your experiment, when you change gloves, and
before you leave lab.
√ Tie back long hair to keep it away from flames and chemicals.
√ Never smoke, eat, drink, or apply cosmetics in the laboratory area. Avoid touching your
face, chewing on pens or other similar behaviors.
√ Never taste a chemical. Check odors, only if instructed to do so, by wafting with one
hand towards your nose.
Behavior
√ Act in a responsible manner at all times. No horseplay or fooling around should occur
in the lab or experimental area. Never remove chemicals from the laboratory area without
proper authorization.
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√ Never perform unauthorized work, preparations, or experiments.
√ Turn off the Bunsen burner when it is not in use. Never leave a flame unattended.
√ Report any injuries to the professor or lab assistant, and seek medical attention
immediately if needed.
√ Keep walkways and work areas free from obstructions. Coats, book bags or other
materials not directly needed to perform labs should be kept in lockers, in the hallway, or
in another room not in use. Keep stools under benches when not in use.
Chemical Hygiene
√ Follow all disposal methods for chemicals as specified by professor or lab assistant.
√ Treat all chemicals with the respect they warrant.
√ In case of spill, immediately contact the professor or lab assistant.
√ Wash off chemicals splashed or spilled on your skin or body immediately and
thoroughly, removing contaminated clothing. Have your lab partner notify the professor
or lab assistant immediately.
√ Clean your lab bench, place all equipment and reagents on the bench as you found them,
and wash your hands with soap and water at the end of each laboratory session.
√ Dispose of all hazardous, Biohazardous, Radioactive and Hazardous Materials, as
directed by your instructor, i.e. blood or bacteria in the “BIOHAZARD” bag, needles in
the “sharps” container, and broken glass in “broken glass” container. There may also be
waste containers for certain chemicals. When in doubt, ask.
Biological Safety
√ Practice Universal Precautions when appropriate. This means that you should assume that
all biological materials are potentially infectious and take necessary precautions to prevent
exposure.
Field Safety
√ Wear appropriate clothing to protect you in the out of doors. Sturdy shoes that cover your
entire foot, long pants, and sleeved shirts are best. Dress for the weather, most of our labs
will be in the field. Wear a hat and sunscreen if needed.
√ Be aware that you are a visitor in the homes of various organisms: especially ticks, bees,
poison ivy, and snakes. Your instructor will orient you to the habits of these organisms in
our area.
√ Please let your instructor know about any pertinent medical history you may have (ex.
diabetes, anaphylaxis, colorblindness). This allows lab staff to be better prepared should
the situation arise.
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CHAPTER 2: USING YOUR SEWANEE ANGELS ACCOUNT
Saving your working files to the Sewanee Angels server
All Sewanee students are provided with a folder and disk space on the Sewanee Angels
server of the university computer system. You can use this space to save important work such as term
papers, class data, and your personal web page. The advantage of saving your work to this folder is
that you can access it from any computer that is hooked up to the university computer system (e.g. in
the library, in a classroom, in your dorm room)
The navigational instructions to access your Sewanee Angels folder differs slightly,
depending upon whether you are using a Mac or a PC computer: If you have trouble accessing your
account, contact the Computing and Network Services help desk (x1369) or the Academic
Technology Center help desk (1362).
Connection Instructions for Macintosh Computers
1.
2.
3.
4.
Click on ‘GO’.
Type angels.sewanee.edu
You will be prompted to give Username and Password.
Type in your username (the same as your email username) and your password (the same
as your email password), then click "OK".
5. A window will appear with a bunch of folders, one of which has your username on it.
Double click on your folder to view the contents.
Connection Instructions for PC Computers
1.
2.
3.
4.
Click on the Start button in the lower left corner of the computer screen and select “Run”
Type in \\angels.sewanee.edu and click “OK”
A window will appear with a prompt for your Username and Password
Type in your username (the same as your email username) and your password (the same as your
email password), then click "OK".
5. A window will appear with a bunch of folders, one of which has your username on it. Double
click on your folder to view the contents.
Adding files to your Sewanee Angels folder
To add files to your folder, do one of the following
a. With your Sewanee Angels folder displayed on the computer, simply drag the file you
have saved on your computer into your Sewanee Angels folder.
b. Do a "Save as" command from whatever application you are using and from the Save
window navigate to your folder before hitting the SAVE key
Retrieving and using files from your Sewanee Angels folder
To retrieve something from your folder
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a. Connect to your Sewanee Angles folder (Mac—In the Chooser select Appleshare, select
Appletalk Zone Sewanee Net Servers, select Sewanee Angels file server, enter your
username and password; PC—In the Start/Run menu type \\angels.sewanee.edu).
b. Drag the file you are interested in using onto the desktop of the computer you are
working on. DON'T WORK ON A DOCUMENT DIRECTLY FROM THE SEWANEE
ANGELS SERVER. Why? If the network connection goes down, you will lose any
unsaved changes. Also, the computer will be slower if it has to work over the network.
c. Save your modified work back to the Sewanee Angels folder when you are
finished.
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CHAPTER 3: LABORATORY NOTEBOOKS
Following are a few hints for keeping your weekly laboratory notebook:
Purpose
State succinctly the goals of the exercise. If you are not sure of the purpose before you come
to lab, leave a few lines for this section, then fill in the purpose when you are confident.
Lecture Notes
You may take lecture notes in your notebook or on other sheets of paper - whichever you
prefer. The lecture notes will pertain to the lab you are doing and future assignments that relate
to the lab. We would suggest that you take lecture notes in your lab notebook so that you do not
have to keep track of separate sheets that could easily be lost. If you feel that some of the notes
would be helpful for you to have in your possession while your lab notebook is being graded you
should photocopy them.
Procedures and Results
These two can usually be combined in the laboratory record. Most of your procedures can be
presented as a table or protocol that shows how you proceeded. A brief narrative is usually
required to supplement a table, but this should include only sufficient information for a
scientifically literate person to repeat your experiment. Do not include details like the tube size
used, but do include the amounts of solutions mixed and their final concentrations. Don’t
describe how to use a spectrophotometer or a balance, but do indicate that you used a Hach DR2500 scanning spectrophotometer to measure absorbance and an electronic top-loading balance
to weigh chemicals.
If you perform calculations, you should show one sample of each kind of calculation. Also
make sure that you include units for parameters with units in the equation. Make sure,
especially, that the correct unit accompanies your final calculation.
Results can be interspersed with the procedures. If you do one portion of the lab and write
those procedures, you may follow that with the results for that section before moving on to write
the procedures for the next segment of the lab. If you find that you must include additional
material for a procedure but have no space for it, indicate clearly the page where the additional
material can be found.
Each table and each graph (called figures) must be formatted properly. All tables should
have a number and title above it (Table 1. Absorbance for pigment A at each wavelength tested.;
see Table 1 below). Units should be found in column titles [Wavelength (nm)]. Any additional
information should be in a caption found below the table. Graphs should also have a number and
title, found below graphs as a caption (Figure 1. Absorption spectrum for pigment A.)
Additional information, such as what each type of symbol or color of line means should also be
in a legend to the figure. In addition, each axis must have a label, and most parameters will
have units. There are many examples of properly labeled tables and figures found throughout the
biology department’s lab manuals.
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Table 1. Absorbance for pigment A at each wavelength tested.
Wavelength (nm)
Absorbance
420
0.12
440
0.34
460
0.55
480
0.22
Answers to problems or questions that appear in the text of the lab
The answers may be reported at the end of the procedures and results section or in the body
of the procedure and results section if you prefer. Many of the problems or questions in this
section are presented in order to get you to make the proper conclusions about the day’s work.
For that reason, some will seem redundant when you write your conclusions.
Conclusions
This is a very important part of your notebook. It is the place where you make your important
observations, and can discuss possible errors or anomalous results. For example, in the
photosynthetic pigment lab, identify each of the pigments you isolated and give your basis for
these conclusions.
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CHAPTER 4: RESEARCH PROPOSAL GUIDELINES
A primary objective of introductory biology labs is to introduce you to, or enrich your
understanding of, the scientific method. At various times throughout the semester, you will
be testing hypotheses by conducting experiments or studies of your own design. These
exercises give you a chance to be creative. At the same time, they require some advanced
planning on your part. Prior to conducting your own study, therefore, you will be required to
turn in a research proposal that justifies and describes your proposed research. This writing
exercise helps you “think through” your experiment, and the proposal itself can be useful
when writing your final lab report because you will have already begun writing some
sections that will be included in your final report. The guidelines provided here are to help
you prepare a research proposal. Note that while these guidelines are to be used for Biology
131/132 proposals, the format for upper level Biology courses may vary slightly, according
to preferences of individual professors. Thus, it is recommended that you consult your
professor for the appropriate proposal format for that upper level course.
This chapter is presented in three sections, Proposal Format for Biology 131, Proposal
Format for Biology 132, and a Sample Proposal. The Proposal Format is presented twice in
order to convey inherent differences between field studies and lab studies; the first
presentation is with Biology 131 examples, and the second with Biology 132 examples
Research Proposal Guidelines: 131 Perspective
A primary objective of introductory biology labs is to introduce you to, or enrich your
understanding of, the scientific method. At various times throughout the semester, you will
be testing hypotheses by conducting experiments or studies of your own design. These
exercises give you a chance to be creative. At the same time, they require some advanced
planning on your part. Prior to conducting your own study, therefore, you will be required to
turn in a research proposal that justifies and describes your proposed research. This writing
exercise helps you “think through” your experiment, and the proposal itself can be useful
when writing your final lab report because you will have already begun writing some
sections that will be included in your final report. The guidelines provided here are to help
you prepare a research proposal.
PROPOSAL FORMAT
• All sections in your proposal, outlined below, should be identified with corresponding
headings.
• Use FUTURE TENSE consistently throughout the text when referring to your
proposed research, as you are writing about activities that you will conduct in the
future. Note that this is in contrast to your lab report, in which you will use the PAST
tense, because your research will be complete when you write the formal report.
• If you are working in a group, use first person plural (we), otherwise use first person
singular (I). E.g. We/I will count the number of flowers produced at the end of an eight
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week growing period. The passive voice may also be used. E.g. The number of
flowers produced will be counted at the end of an eight week growing period.
• All text within your proposal, including the methods, should be written in concise, but
complete, sentences and organized into paragraphs.
• When providing the scientific name of a plant or animal for the first time, remember to
use upper case (capitalize) the first letter of the genus (the species remains in lower
case), and either underline or italicize both the genus and species. After the first
citation, you may simply refer to the common name, or the abbreviated scientific name.
For example, “We propose to examine floral structures of cardinal flowers (Lobelia
cardinalis)”. Thereafter you can just refer to the organism as the cardinal flower, or as
L. cardinalis.
• Number each page and staple or paper clip the pages together, before you hand your
proposal. Check with your instructor on their policy/preferences for line spacing and
printing on the blank side of used paper.
• Refer to the Sample Research Proposal for an example that demonstrates the desired
format, writing style, and approximate text length.
• The format for research proposals outlined in the Student Survival Guide will not be
used in this course. Instead, follow the formatting we provide to you.
PROPOSAL CONTENT
The proposal does not have to be long, but it should be complete, and include all of
the following components: Title, Introduction, Hypotheses, Variables, Methods, Expected
Results, Literature Cited, Data Record Sheet.
Title
The title should be brief but specific. For example: Flower production in full
sunlight- and shade-grown sunflowers (Helianthus spp.) You should not
include the title as a separate page (save the trees!), but it should head the first
page of your proposal. Also include your name, date and pledge on the
proposal.
Introduction For our purposes, a short paragraph (5 – 10 sentences) that provides a
theoretical context for the proposed research will suffice. This will include
background information to justify your hypotheses and proposed methods,
such as what is currently known about the topic. For example, you might cite
a textbook that discusses why sunlight is important for the production of nonphotosynthesizing plant organs such as flowers, or a study showing that
sunflowers require more exposure to sunlight than shade-tolerant bluebells
(Mertensia spp) for growth and development.
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Hypotheses
Hypotheses are possible explanations for observed phenomena. In your
proposal, you will state both a null (HO) and alternate (HA) hypothesis. (Note
that in formal lab reports, it is customary to state only HA). HO is the
hypothesis of “no difference”, while HA predicts that there will be a difference
or association within the data.
For example, in our sunflower experiment, we want to investigate whether
flower production is enhanced by exposure to sunlight. In this case, the
hypotheses might be stated as:
HO: Sunflowers grown in full sun produce flowers of equal number as those
grown in shade. Another way to say this might be "Light level has no
effect on flower production in sunflowers (Helianthus spp.)."
HA: Sunflowers grown in full sun produce more flowers than those grown in
shade.
One cannot “prove” HA, but data can be gathered and analyzed in an attempt
to disprove, or test, HO. If our experiment yields data that give us sufficient
reason to reject Ho, then we accept HA. For example, suppose we grow 10
potted sunflowers in full sun and 10 potted sunflowers in shade, while keeping
all other factors (e.g. sunflower variety, soil moisture or fertility) the same. If,
after statistical analysis, we find that the mean number of flowers produced
per plant is significantly higher for sunflowers grown in full sun than those
grown in shade, then we could reject HO in favor of HA and conclude that
sunlight enhances flower production in sunflowers. Conversely, if we found
that there was no difference in mean flower production between sun- and
shade-grown plants, we would fail to reject HO. This does not mean that we
are disproving HA, only that we do not have the data to reject HO.
NOTE: While you will always be testing HO statistically, your proposed
hypothesis (and the one that you will write about in your final lab report) is
really HA.
Variables
A variable is a characteristic that may differ from one entity to another. In
our experiments, we will be testing the effect of an independent variable X
on dependent variable Y. Technically speaking, the magnitude of the
dependent variable (Y) is assumed to be a function of, or affected by, the
independent variable (X). For this reason, the dependent variable is also
referred to as the response variable.
In the sunflower example, the dependent variable is flower production (mean
number of flowers produced per plant) and the independent variable is light
level, with the "treatments" within the independent variable being exposure to
full sun or shade.
Methods
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In this section, you will describe, in a logical order, exactly what you propose
to do.
Study site – If you are proposing a field study, you will begin the methods
section with a concise description of the study site. It may be appropriate to
mention approximately what time of year the study will take place.
Experimental Design – Here, you describe as clearly and concisely as
possible, the steps necessary to complete your procedure, and to collect and
analyze your data. Write this out in paragraph form, not as a list.
Your experimental design will outline your variables and treatments (e.g.
flower production, sun versus shade), as well as the number of replications per
treatment (e.g. 10), often referred to as “n” or “sample size”. As you describe
your methods, you will refer indirectly to the materials (equipment,
instruments, sample collection materials, etc); do not simply list what you will
use. Summarize all necessary equations in numerical format. You do not
need to explain details such as, “the data will be compiled in an Excel
spreadsheet”, because the reader assumes this. Also you should not include
unnecessary details that could vary from study to study, but would not be
expected to influence the outcome. Some examples if unnecessary details:
• the color of flagging tape used to mark the plants in the field (just say that
plants were marked – someone else may use chalk, or a different color
tape and achieve the same result)
• the length of the measuring tape used to run a transect (here the important
detail is simply the length of the transect)
• the type of knot used to tie off a strip of dialysis tubing (just say that it
was tied off).
Statistical Analysis – Prior to data collection, it is important to determine how
you will analyze your data statistically. In this class, we will be using either a
t-test or linear regression as our primary means of analysis (you will become
more familiar with these as the semester progresses). Thus, in your proposed
methods, you will write a concise statement about the specific statistical
test(s) that will be performed on your data.
If using a t-test, you also need to indicate whether you will use a one- or twotailed test. When you conduct your analysis in Excel, it will give you both
test values, and it is up to you to specify which one you will use. This will
depend upon your alternate hypothesis (HA). Were we to state HA as "Mean
flower production will differ between full sun- and shade-grown plants", but
not specify which treatment we expect to have greater production, we would
use a two-tailed t-test, because we are testing for a difference in mean flower
number in either direction (sun > shade or shade > sun). However, in our
earlier example, we hypothesized that mean flower production would be
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greater in full sun plants compared to shade-grown, so we would use a onetailed t-test, which is generally more powerful.
Thus, our methods statement with regard to the statistical analysis might be:
“Mean flower number per plant will be analyzed using a one-tailed t-test,
assuming equal variances, to determine if significant differences in flower
number exist between sun- and shade-grown plants”.
In some cases we may use linear regression analysis. For example, if we had
used a range of light levels (e.g. 100%, 90%, 70%, 50% and 20% exposure to
full sun) rather than just two (sun and shade), then we could test whether
flower number is related to the amount of light that the plants receive. A
regression analysis would test for this relationship. Here, your methods
statement with regard to the statistical analysis might be: “Linear regression
will be used to determine if flower production in sunflowers is a linear
function of light level exposure during growth".
(Also refer to your Survival Guide for a primer on statistics)
Expected Results – In this section you will provide a concise written statement about each
predicted result, along with either a summary table or a summary figure
depicting the relative trends you expect from your data.
Statements about expected results should be concise but informative and make
reference to the appropriate table or figure. For example: "Mean flower
production will be significantly higher in sun-grown plants than in shadegrown sunflower plants (t-test: p<0.05)".
Tables: In some cases, a data summary table will be more appropriate than a
figure, especially if the data are too complex to present graphically. The table
should always include a descriptive title above the table, as shown in the
example below. The type of summary data that will be inserted (e.g. means ±
standard errors or standard deviations) should be indicated. Since you do not
have any 'real data' yet, and therefore won't have actual values for your mean,
std err or P-value, your summary table will present the trend you expect (e.g.
Sun > Shade). Read Chapters 8 & 10 of the Survival Guide for an explanation
of means, standard errors and P-values.
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Table 1. Mean (+ std err) number of flowers produced by sun- and shadegrown sunflower (Helianthus spp) plants.
Light Level
Flowers/plant
Sun
Shade
Mean + std err >
Mean + std err
n
10
P-value
< 0.05
Note: Tables are easily inserted into the text of a Microsoft Word document
using the “Table” menu. This allows you to control the format much better
than if you were to cut and paste from Microsoft Excel.
Number of flowers/plant
Figure – The figure below depicts the same results as the table above. In this
example, a figure is a more appropriate way to present the data because the
reader can quickly see the expected pattern in flower production (sun >
shade). The figure should have a descriptive title caption located below the
graph. The caption should concisely describe what the figure represents and
include statistics when appropriate. The X- and Y-axes should be labeled
with appropriate units. In this figure, the columns have standard error bars, as
noted in the figure caption.
Sun
Shade
Fig. 1. Mean (± 1 std err) flower production/plant in sun- and shade-grown sunflower
plants (P = ___, n = 10 plants/treatment).
In an example where you are testing the relationship between X and Y, using
linear regression, a figure would be more informative to the reader than a
table. For example, if we had grown the plants under a range of different light
levels, a figure would immediately show us the trend in the data (see Fig. 2
below – flower production increased with increasing plant exposure to light).
A table with the same data would simply be a list of numbers, and would be
more difficult to interpret.
The expected results statement might read something like this: There will be a
significant positive linear relationship between the level of light exposure
plants receive and flower production per plant (R2 = ___, P < 0.05).
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Number of
flowers /plant
The accompanying figure might look like this:
0
20
40
60
80
100
Light Level (%)
Fig. 2. Flower production per plant in the sunflower (Helianthus spp.) as a function
of plant exposure to different light levels (P = ____, R^2 = ___)
Literature Cited – Sources cited (by author, year) in the Introduction section of your
proposal will appear in the Literature Cited section as full bibliographic
citations (refer to Chapter 7 in the Survival Guide for proper formatting).
Data Record Sheet – Designing a data record sheet before you conduct your research helps
you better understand your experimental design and prepares you for more
efficient data collection. This is a table where you will record your raw data
during the study and is different from a summary table of the results. Your
data record sheet should reflect the set up of the spreadsheet that you will use
to enter and analyze the data in Microsoft Excel.
Example of an abbreviated data record sheet (you should include an entire
data record sheet in your proposal)
Plant number
1
2
3
...
10
1
2
3
...
10
Light
treatment
sun
sun
sun
sun
sun
shade
shade
shade
shade
shade
Number
flowers/plant
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Proposal Format: 132 Perspective
• All sections in your proposal, outlined below, should be identified with corresponding
headings.
• Use FUTURE TENSE consistently throughout the text when referring to your proposed
research, as you are writing about activities that you will conduct in the future. Note
that this is in contrast to your lab report, in which you will use the PAST tense, because
your research will be complete when you write the formal report.
• If you are working in a group, use first person plural (we), otherwise use first person
singular (I). E.g. We/I will measure the rate of enzyme action in solutions with pH
measurements of six and eight. The passive voice may also be used. E.g. The rate of
enzyme action will be measured in solutions with pH measurements of six and eight.
• All text within your proposal, including the methods, should be written in concise, but
complete, sentences and organized into paragraphs.
• When providing the scientific name of a bacterium, plant or animal for the first time,
remember to use upper case (capitalize) the first letter of the genus (the species remains
in lower case), and either underline or italicize both the genus and species. After the
first citation, you may simply refer to the common name, or the abbreviated scientific
name. For example, “We propose to examine the enzyme tyrosinase isolated from the
potato, Solanum tuberosum)”. Thereafter you can just refer to the organism as the
potato or as S. tuberosum.
• Number each page, and staple, or paper clip the pages together, before you hand your
proposal. Reuse and recycle paper where possible. Printing your work on the clean
side of used paper is perfectly acceptable, so long as your work is readable.
• A sample proposal available online at the lab web site demonstrates the desired format,
writing style, and approximate text length.
• The Student Survival Guide (available as a download from the course lab web site)
contains additional information on data collection, proposal and report writing, and on
using Microsoft Excel for data analysis.
PROPOSAL CONTENT
The proposal does not have to be long, but it should be complete, and include all of the
following components: Title, Introduction, Hypotheses, Variables, Methods, Expected
Results, Literature Cited, Data Record Sheet.
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Title
The title should be brief but specific. For example: The effects of lowering
pH on the rate of activity of the enzyme, tyrosinase. You should not include
the title as a separate page (save the trees!), but it should head the first page of
your proposal.
Introduction For our purposes, a short paragraph (5 – 10 sentences) that provides a
theoretical context for the proposed research will suffice. This will include
background information to justify your hypotheses and proposed methods,
such as what is currently known about the topic. For example, you might cite
a textbook that discusses why level of pH is important for the enzyme reaction
rate, in general, or a study showing that the reaction rate of tyrosine is affected
by pH in another organism, such as portabello mushrooms.
Hypotheses
Hypotheses are possible explanations for observed phenomena. In your
proposal, you will state both a null (HO) and alternate (HA) hypothesis. (Note
that in formal lab reports, it is customary to state only HA). HO is the
hypothesis of “no difference”, while HA predicts that there will be a difference
or association within the data.
For example, in our enzyme reaction experiment, we want to investigate
whether enzyme action is affected by a decrease in pH. In this case, the
hypotheses might be stated as:
HO: The enzyme tyrosinase, when buffered in solutions of pH 6 and pH 8,
reacts with the substrate catechol to produce a colored product at similar
rates.
HA: The enzyme tyrosinase reacts more slowly with the substrate catechol to
produce a colored product when buffered at pH 6 than it does at pH8.
One cannot “prove” HA, but data can be gathered and analyzed in an attempt
to disprove, or test, HO. If our experiment yields data that give us sufficient
reason to reject Ho, then we accept HA. For example, suppose we combine 4
ml of tyrosine and 4ml of catechol buffered in a sodium phosphate solution
with a pH of 6 and compare the rate of color formation with 4 ml of tyrosine
and 4 ml of catechol buffered in a sodium phosphate solution with a pH of 8.
If, after analysis of our data, we find that the rate of reaction is significantly
higher for enzyme buffered at pH 8 than for enzyme buffered at pH 6, then we
could reject HO in favor of HA and conclude that a pH of 8 is more optimum
for action of the enzyme tyrosinase than a pH of 6. Conversely, if we found
that there was no difference in reaction rates of enzymes in buffers of pH 6
and pH 8, we would fail to reject HO. This does not mean that we are
disproving HA, only that we do not have the data to reject HO.
20
NOTE: While you will always be testing HO statistically, your proposed
hypothesis (and the one that you will write about in your final lab report) is
really HA.
Variables
A variable is a characteristic that may differ from one entity to another. In
our experiments, we will be testing the effect of an independent variable X
on dependent variable Y. Technically speaking, the magnitude of the
dependent variable (Y) is often assumed to be a function of, or affected by, the
independent variable (X). For this reason, the dependent variable is also
referred to as the response variable.
Independent variable treatments are the various levels or values of
independent variable X that you plan to test. They should be specified in your
proposal along with the independent variable.
In the enzyme example, the dependent variable is enzyme reaction rate and
the independent variable is the pH level of the buffer to which the enzyme is
added, with the treatments within the independent variable being the various
pH levels you plan to test (eg. pH 6.0, 7.0, 8.0, 9.0 and 10.0).
Methods
In this section, you will describe, in a logical order, exactly what you propose
to do.
Experimental Design – Here, you describe as clearly and concisely as
possible, the steps necessary to complete your procedure, and to collect and
analyze your data. Write this out in paragraph form, not as a list.
Your experimental design will outline your variables and treatments (e.g.
enzyme reaction rate, pH 6 versus pH 8), as well as the number of replications
per treatment (e.g. 10), often referred to as “n” or “sample size”. As you
describe your methods, you will refer indirectly to the materials (equipment,
instruments, sample collection materials, etc); do not simply list what you will
use. Summarize all necessary equations in numerical format. You do not
need to explain details such as, “the data will be compiled in an Excel
spreadsheet”, because this is assumed by the reader. Also you should not
include unnecessary details that could vary from study to study, but would not
be expected to influence the outcome. Some examples of unnecessary details:
•
•
•
•
the size of the test tube into which you mixed your enzyme and buffer solution
the fact that you had to push the blue button to turn on the spectrophotometer
the type of knot used to tie off a strip of dialysis tubing (just say that it was tied off)
the number of each tube. The important detail here is what is in each tube (e.g. the solution, and/or
its concentration) or its treatment (e.g. boiled, frozen, untreated)
Statistical Analysis – Occasionally, you will be required to analyze your data
statistically, but for the most part in Biology 132, your comparisons will be
qualitative rather than quantitative. If you are required to conduct statistical
analysis, then prior to data collection, it is important to determine how you
will analyze your data statistically. In this class, we will most likely be using
21
either a t-test or linear regression as our primary means of analysis (you will
become more familiar with these as the semester progresses, if necessary).
Thus, in your proposed methods, you will write a concise statement about the
specific way in which you will analyze your data. For example, is it a
qualitative comparison of reactions rates, or mean heart rates, or does your
analysis involve a statistical comparison of reaction rates or mean heart rates.
If it is a statistical test analysis, what test are you using. In this latter
statement, you should include your predetermined alpha (α), or
“significance” level, which indicates the level of risk you are willing to accept
when declaring statistical differences among treatments. Generally, this value
is ranges from 0.001 to 0.05, or may be as high as 0.10, depending upon the
type of experiment (and the stakes involved). An α = 0.05 means that there is
a 5% chance of finding a significant difference among treatments by random
chance, when in fact, this difference does not exist. In other words, you have
a 5% chance of rejecting HO when HO is really true. Your lab instructor will
let you know if a statistical test is needed, and what the appropriate α level is.
Also refer to your Survival Guide, Chapter 9 for a primer on statistics.
Expected Results – In this section you will provide a concise written statement about each
predicted result, along with either a summary table or a summary figure
depicting the relative trends you expect from your data.
Statement: The expected results statement should be concise, but complete,
and include an indication of the general trend you expect to find, based on
your alternate hypothesis. For example: "The rate of enzyme action will be
higher in a solution of pH 8 than in a solution of pH 6 (Table 1)".
Tables and Figures: In addition to the results statement (to which you should
make reference in your expected results statement), you will need to include a
summary of your results, either in the form of a table or a figure. Tables are
more appropriate if the data are too complex to present graphically. The table
should always include a descriptive title at the top of the table, as shown in the
example below. Means ± standard errors, or standard deviations, when
appropriate, are included, and identified as such in the table title. Since you
do not have any 'real data' yet, and therefore won't have actual values for your
mean, std err or P-value, your summary table will give present the generalized
format of how your actual data will appear in your formal lab report. Read
Chapters 7 & 9 of the Survival Guide for an explanation of means, standard
errors and P-values.
22
Table 1. Mean (+ std err) reaction rate of the enzyme tyrosinase extracted
from potato (Solanum tuberosum) with catechol substrate when buffered at
varying pH levels.
Reaction rate
pH 6
pH 8
Mean + std err >
Mean + std err
n
P-value
10
< 0.05
Note: Tables are easily inserted into the text of a Microsoft Word document using the “Table” menu.
This allows you to control the format much better than if you were to cut and paste from Microsoft
Excel.
Reaction rate
Absorbance (min)
Figure – The figure below depicts the same results as the table above. In this
example, a figure is a more appropriate way to present the data because the
reader can quickly see the magnitude of the different in reaction rates at the
two pH levels. The figure should have a descriptive title located at the bottom
of the graph. The title should include statistics, when appropriate. The Xand Y-axes should be labeled with appropriate units. In this figure, the
columns have standard error bars.
8
6
buffered pH
Fig. 1. Mean (± 1 std err) reaction rate of tyrosinase with catechol substrate (P =
___, n = 10). Tyrosinase enzyme was isolated from potato ( Solanum tuberosum )
immediately prior to use.
In an example where you are testing the relationship between X and Y using
linear regression, a figure is definitely more informative to the reader than a
table. For example, if we had examined reaction rate over a range of different
pH levels, a figure would immediately show us the trend in the data (see Fig.
2 below – enzyme activity increases with increasing pH). A table with the
same data would simply be a list of numbers, and would be more difficult to
interpret.
The expected results statement might read something like this: There will be a
significant positive linear relationship between the activity of the enzyme
tyrosinase and pH of the buffered substrate (Figure 2).
The accompanying figure might look like this:
Absorbance (min)
Reaction rate (¶
23
0
2
4
6
8
10
12
Buffered pH
Fig. 2. Reaction rate of the enzyme tyrosinase with catechol substrate
a function of buffer pH (r2 = ___, P < 0.05). Tyrosinase was isolated
fpotato (Solanum tuberosum) immediately prior to use.
Literature Cited – Sources cited (by author, year) in the Introduction section of your
proposal will appear in the Literature Cited section as full bibliographic
citations (refer to Chapter 7 of the Survival Guide for formatting).
Data Record Sheet – Designing a data record sheet before you conduct your research helps
you better understand your experimental design and prepares you for more
efficient data collection. It also helps prepare for analysis after you have
collected the data: Your data record sheet should reflect the set up of the
spreadsheet that you will use to enter and analyze the data.
Example of an abbreviated data record sheet (you should include an entire
data record sheet in your proposal)
Data record sheet for 1 potato sample: repeat table for each of 10 samples.
pH 6
pH 8
Minute
Elapsed Abs.** ∆ Abs.**
Minute Elapsed
Abs.
Time
Time
(min:sec)
(min:sec)
0
1
2
3
10
0
30
1:00
1:30
2:00
2:30
15
45
1:15
1:45
2:15
2:45
3:00
...
10:00
3:15
...
10:00
Reaction rate (/min) = ______________
∆ Abs.
Reaction rate (/min) = ______________
24
Sample Research Proposal
Name:
Pledge:
Title Flower production in full sun- and shade-grown sunflowers (Helianthus spp.)
Introduction
Plants are photoautotrophs that use light energy to drive the process of photosynthesis,
although the amount of light necessary for normal growth and development vary among
species. Shade intolerant plant species have higher light compensation points (LCPs) than
shade tolerant plants, which means that they need a greater amount of photosynthetically
active radiation (PAR) for simple maintenance respiration and growth (Kozlowski et al.
1991). Beaubaire (1997) found that wild ginger (Asarum caudatum), which normally grows
in dense carpets in the understory of redwood forests, suffered photoinhibition and high
mortality when grown in full sun, while light-demanding cardinal flowers (Lobelia
cardinalis) would not grow in less than 70 percent full sun. In general, sexual reproduction
requires a large energy investment by the plant that is often dependent upon stored reserves
because organs such as flowers and fruits do not photosynthesize (Pearcy et al. 1989).
Common sunflowers (Helianthus spp.) are a plant favored by many flower gardeners.
Because most residential gardens in Sewanee, Tennessee, are shaded by oak trees, we
propose to examine whether a lack of sunlight would greatly reduce flower production by
common sunflowers. Because flower production requires a large energy investment by any
plant, we will also examine whether mean flower mass per plant decreases as the number of
flowers produced on full sunlight-grown plants increases.
Hypotheses
1.
HO: Sunflowers grown in full sunlight produce the same number of flowers per plant as
those grown in shade.
HA: Sunflowers grown in full sunlight produce more flowers per plant than those
grown in shade.
2.
HO: Flower mass of full sun sunflower plants is not a function of the number of flowers
produced per plant.
HA: Flower mass of full sun sunflower plants decreases as the number of flowers per
plant increases.
Variables
Hypothesis 1: Dependent variable = flower number (# of flowers produced per plant)
Independent variable = sunlight exposure
Independent variable treatments = exposure to full sunlight vs. 85% shade
Hypothesis 2: Dependent variable = mass (g) of flowers produced on full sun-grown plants
25
Independent variable = number of flowers produced per full sun-grown plants
Methods
Experimental Design. To compare flower production between sun- and shade-grown
sunflower plants, we will expose ten 4-week old sunflower plants to 8 hours of full sunlight
in a greenhouse for a total of 30 days. An equal number of the same variety of sunflower
plants will be grown in the same greenhouse under a mesh cloth which permits only 15%
PAR emission. We chose 85% shade for this treatment because it approximates the
conditions of typical tree-covered gardens of Sewanee residences. Prior to treatment, the
plants will be germinated and grown in the same 40% loam potting soil mix and will be
watered daily with 500 ml of 0.5 M Hogan’s fertilizer solution to ensure that nutrient
deficiencies do not inhibit flowering in either treatment. After 30 days, we will count the
number of flowers on each plant. The flowers will then be excised at the base of the flower
head, placed into separate labeled paper bags, and dried at 60 degrees C° for 48 hours. The
dried flowers from full sun-grown plants will be weighed to quantify mean flower dry mass
per sunflower plant.
Statistical Analyses. Mean flower number per plant will be analyzed using a one-tailed t-test,
assuming equal variances, to determine if sun-grown plants produce significantly more
flowers than shade-grown plants. Mean flower weight per plant from full sun-grown plants
will be used in linear regression analysis to determine if flower mass is a function of the
number of flowers on a plant.
Expected Results
Effect of light level on flower production – The mean number of flowers produced per plant
will be significantly higher in sun-grown than in shade-grown sunflowers (P <0.05, Fig. 1).
14
12
10
8
6
4
2
0
Full sun
Shade
Fig 1. Mean flower production (± std err) in full sun- and shade- grown sunflower
(Helianthus spp) plants (n=10, p = 0.014)
Effect of flower production on flower size – Mean flower mass for sun-grown plants will
decrease with increasing number of flowers produced per plant (P < 0.05 , R2 = #.## , Fig. 2).
26
0
Number of flowers per plant
0
Fig. 2. Mean flower mass (g) of sunflower plants (Helianthus spp.) grown in full sun as
a function of the number of flowers produced per plant (P = ___, R^2 =___).
Literature Cited
Beaubaire, J. 1997. Sun requirements for wildflower gardens. Accessed Oct. 13, 1998.
http:/www.bbg.org/topics/wildflowers/html.
Kozlowski, T. T., P.J. Kramer, and S.G. Pallardy. 1991. The Physiological Ecology of
Woody Plants. Academic Press Inc., San Diego, CA, USA.
Pearcy, R.W., J. Ehleringer, H.A. Mooney, and P.W. Rundel. 1989. Plant Physiological
Ecology. Field Methods and Instrumentation. Chapman and Hall, London, England.
Data Record Sheet
NOTE: there is no fixed design for a data sheet – use your creativity, but strive for clarity,
ease-of-use during data collection, and ease-of-transfer of data into the computer later. Your
data sheet will be empty until you collect your data but in the example below some sample
numbers have been inserted to illustrate how the sheet will be used.
Plant
No.
1
2
3
4
5
6
7
8
9
10
No. flowers
Full sun
Shade
7
12
2
1
Mass per full sun-grown flower (g)
25, 30, 22, 18, 15, 22, 25
17, 12, 15, 16, 8, 20, 13, 15, 20, 17, 22, 9
Mean
flower
mass (g)
27
CHAPTER 5: A GUIDE TO WRITING SCIENTIFIC PAPERS 1
This guide is intended to supply the student with a model for writing a paper on scientific
research. Consider this guide to be an acceptable starting point. It will work well for any
class taken in this Department of Biology.
Goals
The writing of scientific paper is based on several simple concepts. The main purpose of
a scientific paper is to tell the reader why you did the experiment, what you did, how you did
it, the results, and the significance of those results. The style emphasizes conciseness and
clarity of language, so words must be chosen to say exactly what you mean them to say, with
no tolerance for vagueness or "you know what I mean." The organization of a paper is
logical, with rules for what goes into each section. Finally, factual material (results) and the
interpretation of factual material (discussion) are clearly separated into different sections and
never allowed to become confused. Remember these goals as you write a paper, and judge
your own work by them before you complete it. In addition, a paper should be written in
PAST TENSE as it is something that you have already done.
Sections of a Paper
Though individual papers will vary in some details, the basic arrangement of papers in
this course is usually the following:
Title
Abstract
Body of the Paper:
Introduction
Materials and Methods
Results
Discussion
Literature Cited
Tables, Figures and Legends
Each section, other than the title page, should bear the heading for that section, e.g.
“Materials and Methods." Papers should be paginated with the title page as page one and all
subsequent pages numbered accordingly.
Title
◊ Title should appear at the top and be brief but should inform the reader of the subject (but
usually not the results) of the work being reported. Be specific. "Franklin County Flora"
is too broad for a project that is really "Differences in Flora at Different Elevations in
Franklin County, Tennessee."
◊
The author's full name and the full names of all partners with whom data were shared (if
any).
◊
The date.
1
Adapted from a version written by Dr. Timothy Keith-Lucas and used with his permission.
28
◊
The pledge and signature of the author only.
Abstract
The abstract should be on the same page just after the title. The abstract is a brief
(approximately 150-250 words) summary of the entire paper. The simplest way of writing an
acceptable abstract is to summarize in turn each of the four major sections of the report
(Introduction, Methods, Results, and Discussion) in, at most, two sentences each. Don't
forget to include the results here.
Introduction
A typical (it varies) Introduction has three parts. First, in a brief paragraph, give general
information relating to your topic. This should also include a general question or issue of
interest. Note that this may be a general question about the way the world works, rather than
a narrow question applicable only to one locality. You may be attempting to answer this
question in a very narrow set of circumstances in one place, but your interest as a scientist is
in broader questions about the world. It is most important, not just to the paper but to the
success of the project in general, that this question or goal be clearly stated.
Having introduced a topic, now describe what is known about the topic prior to the
beginning of your project. Typically, this part is a literature review, describing what others
have found when they have studied similar issues in other locations. This section might
include your logic for suspecting a particular result. Be sure to cite properly work which has
already been published (see the Literature Cited section of this guide). Do not go into the
details of Methods, report results or reach conclusions in the Introduction.
Finally, write a short paragraph telling the reader what your specific hypothesis is, and
how you tested the hypothesis you have posed and reviewed above. "By analyzing the flora
at five elevations in Franklin County, Tennessee, we tested the hypothesis that flora
composition varied with slight changes in elevations" could be the lead sentence in this
section. Be brief, and leave the details for the Methods section.
Note the logic in the paper thus far. Introduce the reader to the topic, describe what is
already known, and finally, state your hypothesis and how you intend to test it. Now the
reader is ready to read the details of your work and will understand the intent behind your use
of various methods.
Materials and Methods
In this section you need to describe your work in sufficient detail to allow the reader to
replicate the study. That means that any detail that could influence your results needs to be
included. An example would be the size of the sample taken or the season in which the study
was performed, what solutions you used and what their concentrations were. The reader may
need to read instruction manuals for equipment used, but they should know what equipment
to use, volumes, weights, doses, settings, numbers of subjects and treatments, etc. Details
that would not reasonably be expected to influence the results should be excluded. This
requires judgement. You must ask yourself whether a detail could influence the results, and
you must describe an experiment clearly to someone who was not present when you were.
29
Describe the steps taken to complete the study. Avoid giving instructions ("Dip the pH
meter in the sample.") when procedurally what happened was that the pH was measured.
Again, include everything necessary to replicate the work, and nothing more. This is NOT to
be written giving instructions as if it is a laboratory manual. Each step of the methodology
should have its own paragraph and each paragraph should have a topic sentence, other
sentences that are on that topic and give more details, and then a final sentence. A methods
section that starts with “These are the methods I will use” and then goes on in one long
paragraph is not acceptable.
Methods should include any formulas that you used for calculations. Include not only the
formula with the variables [with all variables defined, e.g. M is the mass (g)] but also one
sample of the formula with all the numbers substituted for the variables. Do this for all the
formulas you use (except statistical formulas). Any statistics you use should be stated in the
methods. For statistics, the formulas are generally well known, so you do not need to give
statistical formulas, just state that you found the mean + S.E. (standard error or standard
deviation – S.D. or whatever) and any test that you used to determine statistical differences
between values (t-test, ANOVA, etc.).
Again, note the logic. Now you have described a specific experiment with which you
hope to answer the question posed and reviewed in the Introduction.
Results
In the Results section describe what you got when you carried out the procedure just
described in the Materials and Methods section. Report the results factually (no
interpretation). Be quantitative (use the numbers), and leave your conclusions ("...because
temperatures are lower at high elevations") for the Discussion.
The results will not be merely a compilation of tables and graphs. Although most of your
results will appear in tables and figures (for example, graphs or drawings), a person reading
the text of your Results section should understand the results of your study without having to
study your figures and tables. Figures should support the text and supply details, but what
happened should be clear from the text itself. Never, ever, start a Results section with "The
results are summarized in Figures 1 through 4." When used, figures and tables should clearly
make your point, usually by comparing the results found under two or more conditions, and
should be clearly labeled and cited in the text where appropriate. Avoid the temptation to
pile in irrelevant figures.
Refer to figures and tables in the text of your Results by their numbers. You do not have
to write “See Figure 1,” just say something like “The osmotic concentration of the bodily
fluids was higher in x than in y (Figure 1).” Figures and tables should be numbered
separately, a series of numbers for figures and a series of numbers for tables. In addition,
they should be numbered in the order in which they are referred to in the text. Never
refer to Table 2 before you refer to Table 1. Renumber your tables if you need to!! Never
have a table or a figure to which you do not refer your reader!! You must make reference to
all tables and figures.
30
Each table and each graph must be formatted properly. Tables should have a number and
title above it (Table 1: Absorbance for pigment A at each wavelength tested.). Units should
be found in Column titles (Wavelength (nm)). Any additional information such as
abbreviations should be in a caption found below the table. Graphs or figures should also
have a number and title, although these are often found below them as a caption (Figure 1.
Absorption spectrum for pigment A.) Additional information, such as what each type of
symbol or color of line means and any abbreviations should be given in a legend. In
addition, each axis must have a label, and most parameters will have units.
Tables and figures should be found on separate sheets at the end of the paper. The table
number and title with all needed information (such as abbreviations, etc.) should be on the
same page as the table. The figure number should be on the same page as the figure. A list
of figure legends can all be on one separate sheet at the end of the paper, or each figure
legend can be on the same page as the figure. This is the way scientific journals require
manuscripts to be formatted when submitted for publication.
Discussion
You have stated a hypothesis, described a specific experiment to address it, and reported
the results of that experiment. Now it is time to discuss whether or not the data support the
hypothesis. In the first paragraph you should start with a restatement of your hypothesis,
why it was posed and whether or not your data support or do not support the hypothesis. It is
best not to keep your reader guessing about whether or not the hypothesis was supported. In
subsequent paragraphs, you should argue for your conclusion(s) with all the logic and data
(from the results) that you can command.
NEVER say that you have proven ANYTHING in your paper. You have not. You have
merely gathered evidence that either supports you hypothesis (fails to reject) or fails to
support (rejects) your hypothesis.
You must also deal with alternative explanations for the results (such as having observed
at different times of day in the two sites) and with weaknesses in your design, as in "The
study would have been improved by a larger sample." If your conclusion is that your
hypothesis was wrong, say so and why. In addition, data are never wrong (as long as there
were proper controls and the methods were followed properly). They are just your data. Do
not do a lot of data-bashing in your discussion. There is not a requirement that research turn
out the way you expected it to. It is far more scientific to report your data accurately than to
obtain conclusive results. It is OK to pose further hypotheses to be tested in the future.
Literature Cited
You are required to learn how to cite references, both in the text and at the end of the
paper. The style presented here is the same as that used in the journal Ecology. Notice that it
is different from styles used in English and other humanities courses. Refer to Chapter 7:
Literature Cited for instructions and examples of correct citations to be used in all
assignments.
31
Note that you have come full circle. You started with a question and ended with the
answer to it or with a hypothesis and ended either supporting or failing to support it. Thus is
the logic of report writing.
Tables and Figures
Tables and figures are elements of your work that allow you to summarize (tables) or
graphically represent (figures) your data in a way that will give your reader a ‘bottom line’
understanding of your work. Each has it’s own major parts with some important guidelines
to remember.
Tables within a paper should be numbered (Table 1, Table 2 etc.) in the order that they
appear in the paper. Number and title are located directly above the table. Titles should be
unique for each table, and should tell the reader about the data. (Table 1 Index of similarity
for diets of 3 sympatric salamander species.) Located above each column is a column
heading, identifying the data in the column; usually just a word or short phrase. These are
commonly the dependent variables. Row headings are found in the first column of the table.
This column is also called the “stub”. These headings are often the independent variables
and are also just a short phrase. The body of the table, or the field, contains the data.
Footnotes, denoted with a symbol and located directly beneath the table, should be used to
clarify notations or other ambiguities in the table. (* Comparisons were made using the five
most abundant prey items for each species.)
Figures are also numbered in the order in which they appear in a paper (Figure 1, Figure
2 etc.) and have a title as in tables. The caption consists of the Figure # and the title together,
and is also a phrase or sentence fragment describing what the data show. (Figure 1. Five most
abundant prey items for 3 sympatric species of salamander.) The caption is located below a
figure. You’ll use figures mostly to illustrate trends or proportions, but they also include
photographs, drawings or other illustrations. Axes as well as legends should be clearly
labeled including units for axes and symbols used in legends.
Tables and figures should be indirectly referenced in the text in which they are discussed
using parentheses and the figure or table number (Table 2) or (Fig. or Figure 2).
Formatting
In scientific papers as in proposals, certain things need to be formatted very specifically.
Please refer to Chapter 5 Research Proposal Guidelines for further format guidelines and
examples.
33
CHAPTER 6: LITERATURE CITED
You are required to learn how to cite references, both in the text and at the end of the
paper, according to the format used in the peer reviewed journal Ecology, as adopted by this
department. Notice that it is different from styles used in English and other humanities
courses.
Citations Within Text
There are several ways to cite the sources of ideas within the text of the report. Either
they are numbered and the number of the reference is found in the text (e.g. as in Science and
Proceedings of the National Academy of Science), or they are cited by the name-and-year
system (most biology journals). You will use the name-and-year system for Biology 131
and 132. Following are some examples of how to cite references within text.
1. When there is one author: "Jones (1983) showed that . . ." or "Previous work
(Jones 1983) shows . . ."
2. When there are two authors, use the last names of both authors when citing, as in
"Jones and Doe (1983) reported . . ." or "as previously reported (Jones and Doe
1983) . . ."
3. When there are more than two authors, use only the last name of the first author,
followed by et al., as in "Jones et al. (1983) report that . . ." or "Some
investigators (Jones et al. 1983) have reported . . ." NOTE the format of et al.: it
is italicized and there is a period after al. The expression et al. is Latin for "and
others."
4. When there are two or more references showing similar results, list the
references chronologically, separated by a semicolon: "This has been previously
reported (Jones 1983; Smith and Doe 1985) . . ."
The format of citations must be followed precisely; down to the commas and the use of
italics.
Literature Cited Page
At the end of the paper you will have a Literature Cited section. The format of your
citations must conform to the following formats. Pay close attention to what is or is not
capitalized or italicized, where periods, commas, colons and semicolons appear, and the
order in which the information (such as authors and their initials, etc.) appears in the citation.
With regard to Authors’ names – use initials only, do not give full first and/or middle names.
Also, the authors must be in the proper order. The first author listed on a paper or book must
be the first one you list within the citation.
In your Literature Cited section, alphabetize entries according to the last name of the first
author of each work cited. If you have multiple sources from the same author(s), arrange by
34
year (earlier first); if you have multiple sources from the same author(s), in the same year,
add a lower case letter to identify each when citing in the text and when making the
bibliographical entry (Jones 1983a, Jones l983b).
In addition to the formatting discussed above, Latin names, including species names, are
underlined or italicized. Note that the last names of all authors of a paper are included in the
Literature Cited section, even though the names of only one or at most two authors are cited
in the text of the report (e.g.: “Berner et al. 1999” would be found in the text, but “Berner,
N.J., D.A. Grahn, and H.C. Heller. 1999. 8-OH-DPAT-sensitive neurons in the nucleus
raphe magnus modulate thermoregulatory output in rats. Brain Research 831:155-164.”
would be found in the Literature Cited section.)
Standard Journal Article
Single author
Generic format:
Author #1, A.A. Year. Title of article. Title of Journal Volume:pages.
Example:
Haskell, D.G. 1995. Re-evaluation of the effects of forest fragmentation on rates of bird-nest
predation. Conservation Biology 9:1316-1318.
Two or more authors (separate last and next to last author names by 'and')
Generic format:
Author #1, A.A., B.B. Author #2, and C.C. Author #3. Year. Title of article. Title of Journal
Volume:pages.
Example:
Berner, N.J., D.A. Grahn, and H.C. Heller. 1999. 8-OH-DPAT-sensitive neurons in the
nucleus raphne magnus modulate thermoregulatory output in rats. Brain Research 831:155164.
Books
Note: the title of the book is neither underlined nor italicized.
Personal Author(s) (e.g. the entire book is written by the author(s))
Generic format:
Author #1, Author #2 and Author #3. Year. Title of book. Edition (if applicable). Publisher,
City, Country.
Example:
Eason, G., C.W. Coles, and G.Gettingby. 1980. Mathematics and statistics for the biosciences. 5th ed. Ellis Horwod Limited, West Sussex, England.
Edited book (e.g. a book in which different authors contributed individual chapters)
Generic format for citing an individual chapter:
Chapter Author #1 and Chapter Author #2. Year. Title of chapter. Pages XX-YY in Editor(s).
35
Title of book. Edition (if applicable). Publisher, City, Country.
Example:
Hammerschmidt, R and R.L. Nicholson. 1999. A survey of plant defense responses to
pathogens. Pages 55-71 in Agrawal, A.A., S. Tuzun and E. Bent, editors. Induced plant
defenses against pathogens and herbivores: biochemistry, ecology and agriculture. APS
Press, St. Paul, USA.
Example for citing the entire book (rather than an individual chapter):
Wood, R.K.S., editor. 1982. Active defense mechanisms in plants. Plenum Press, New York,
USA.
Items From The World Wide Web 1
You should be cautious when looking for sources on the internet. It is important that
articles used are from reputable, peer reviewed web sites. Remember, any Billy Bob can put
anything they want to, information or misinformation, on the world wide web.
Note that articles that are accessed online from journals that are also available in
print version (e.g. Ecology, Conservation Biology, Molecular Biology of the Cell) should
be cited according to the printed journal format given above, and not as a web
document. Only periodicals that are available only as an electronic source should be
cited as web documents.
When citing web sites, include the author(s), year (if available), title of web page,
retrieval date of the information and the Universal Resource Locater (web address) or URL.
Generic format:
Author, A.A. Year. Title of work. Retrieved month day, year. URL.
Note: If the web site does not have an obvious author, cite the author as Anonymous
Example:
Anonymous. Year. Title of work. Retrieved month day, year. URL.
Brown, J. 1996. Bugs in the News. Retrieved October 21, 1996. http://falcon.cc.ukans.edu/~jbrown/bugs/html.
1
Pechenik, J.A. A Short Guide to Writing About Biology. 3rd Edition. Longman Press, N.Y. 1997.
36
CHAPTER 7: AN INTRODUCTION TO GRAPHS AND DATA
SUMMARIES
This tutorial will introduce you to some of the tools that biologists use when we present
and summarize their data.
Part 1: What items are considered data?
Before you jump into drawing graphs and calculating averages, it is important that you
understand the nature of your data. Here are the three main categories:
Continuous data
These are numbers that can take on any value (i.e. fractions are allowed). For example:
•
•
•
•
human height measured with a ruler
temperature measured with a thermometer
diameter of hickory nuts measured with calipers
proportion of time a squirrel spends feeding
Discrete data
These are all non-continuous data. There are two main classes:
Categorical Data: These occur in non-overlapping, distinct categories or classes. These
categories are not usually numerical. For example:
•
•
•
color of bird feathers classified into red, blue or brown
sex: male or female
a bird nest classified as either having been preyed upon, or having successfully
reproduced young birds.
Counts: This type of data is numerical, but not continuous
• the number of acorns found in a square meter of forest. Here we cannot have “half
acorns”, so the data are not continuous.
• the number of birds seen on a census
Part 2: Show me your data!
Descriptive statistics
Descriptive statistics should convey information about data in an easy-to-understand and
digestible way. We encounter descriptive statistics everyday: Grades and GPA’s are
supposed to summarize the results of countless tests and papers, stock market indices (e.g.,
the Dow Jones average) summarize the performance of some aspect of the stock market, “last
frost” dates are used by farmers and gardeners to plan their planting, newspapers publish
“crime statistics” which may summarize criminal activity in our communities. All these
summaries are descriptive statistics. Later in the course you will study inferential statistics or
"statistical tests" which are designed to test for differences between groups, or look for
correlations between variables.
There are many methods of summarizing data, and our choice of method will affect the
message that we convey with our data. Both producers and consumers of descriptive
statistics should know how the choice of descriptive statistic affects this “message”.
37
Producers of information should be aware so that they can choose the most appropriate (or
deceptive, if you are in the spin-doctoring trade!) method. Consumers of information should
be aware so that they can evaluate the information that is given to them. For example, if a
College claims that its “average class size is 12”, the question that should jump to the front of
your mind is: what kind of average?
The nitty-gritty: when we are summarizing data we might want to convey information
about the following:
♦ the “average”
♦ how much variation is present in the data
♦ trends in the data
♦ relative proportions of categories in the data
The “average” of continuous numbers and counts can be summarized as follows:
Mean: Add up all the numbers in your sample and divide by the sample size. The
mean gives a measure of the “center of gravity” of data. You can use this with
continuous or count data.
Median: Line up the numbers from biggest to smallest (i.e. put them in rank). The
number in the middle is the median. The median is also called the 50th percentile. You
can use this with continuous or count data.
Mode: This is the most common number. For categorical numbers you can report the
mode (the category with the largest number of entries) but not the median or the mean.
Variation can be measured in the following ways:
Range: This is the difference between the two extreme values in a sample
Standard deviation (s.d.): This is a more involved measure of the variation in a sample.
To get the s.d. we calculate the difference between the value of each data point and the
mean of all the data points. Although it is not important for our purposes to know the
exact formula, the s.d. is the square root of these values divided by the sample size. The
important thing to note is that the s.d. is larger in more variable datasets than in more
uniform datasets.
Example: Here are some continuous data arranged in rank:
0, 0, 0, 0, 1, 1, 1, 1.3, 1.4, 1.5, 1.9, 2, 2.4, 2.4, 2.6, 3, 3.5, 3.5, 4.3, 5, 6.5, 8, 9, 9.7,
12
The sample size is 25
The mean of this data is 3.36
The median is 2.4
The mode is 0
The standard deviation is 3.37
The range is 12
Look at that! The mean is higher than the median. This happened because our data are
skewed (skewed = not arranged symmetrically around the mean). Now, if you were an
employer hoping to attract a new employee and these data represented the salaries of
employees in your company in units of $10,000, you might use the mean and say “The
38
average salary in my company is $33,200”. On the other hand, if the data represented
student debt after 1 year in college in $1000 units, a college recruiter might use the median
and say “The average student debt after one year at our College is only $2,400”. Neither
person lied, both reported real averages. So, which is the “right” average to use? The answer
to this question depends what you want your summary statistics to represent. The median
will always have half the data points above it and half the data points below it. The mean
will show the “center of gravity”. In general, if the frequency distribution of your data is
bell-shaped, the mean and the median will be about the same. If you have one or two very
extreme values in your data (e.g., in the example above if we had a value of 120), the mean
will be strongly affected by this value, whereas the median will not.
To plot the mean and standard deviation on a graph use a “dot” for the mean, and two
lines for the standard deviation. Each line is exactly one standard deviation long as the
following graph shows:
8.00
Salary in Thousands
7.00
6.00
5.00
4.00
3.00
2.00
1.00
0.00
Figure 1. Summary of our data (showing mean and one standard
deviation).
This graph only has one data point and therefore does not convey much information.
Graphs are most useful when you want to summarize large amounts of data in an easy to read
format. Before you go on to study some different types of graph, please review the following
guidelines which apply to all graphs:
General guidelines for all graphs
i.
ii.
iii.
Label each axis, include the units of all measurements.
Give the graph a title. The title should do more than repeat the axis labels.
Scientific journals do not publish 3-D graphs unless there really are three axes. 3dimensional graphs may look fancy, but they are hard to read.
39
Overview of three types of graph:
In this section you will learn about scatter plots, line graphs, and bar charts. Each type of
graph is useful in a different situation. Here is a quick summary of when to use each type of
graph:
Table 2. Basic graph types and when to use them.
Graph type
Scatter plot
Use this graph…
For continuous or count data: shows relationship between
two sets of numbers
For continuous or count data when we want to look at the
details of "ups and downs" in the data.
When data are arranged in categories and you wish to
present a summary for each category
Line graph
Bar chart
Scatter plot: Usually used when we want to show the relationship between two groups of
numbers that are continuous or count data. For example:
Volume (mm)
35
30
25
20
15
10
5
0
0
5
10
15
Age (hours)
20
25
Figure 2. Volume of milk consumed by day-old piglets plotted against
piglet age.
Sometimes we may want to draw a “best” line through the points. One such method is the
“best straight line” method. Be careful - only use “best straight lines” when you have good
reason to expect a linear relationship. A common mistake is drawing straight lines through
data that are not linear.
Here are the piglet data with a best fit line drawn through them. There are statistical
methods that allow precise drawing of best fit lines -- the most common is the called "least
squares" method. We will discuss this method in more detail in the section on inferential
statistics.
40
35
30
25
Volume (ml)
20
15
10
5
0
0
5
10
15
20
25
Age (hours)
Figure 3. Volume of milk consumed by day-old piglets plotted against piglet age
(with best fit line).
Line graph: This is essentially a scatter plot with the “dots” connected. This graph is
usually used when we want to show how one variable (on the y-axis) varies with changes in
another (on the x-axis). Usually both axes are continuous or count data. Line graphs connect
all the points, so this type of graph is only suitable when we are interested in the fine details
of how our data move up and down. Scatter plots should be used when we are more
interested in general trends. Also note that line graphs only work when there is just one yvalue associated with each x-value (scatter plots can have more than one y-value associated
with each x-value). For example:
20
Weight of honey (kg)
18
16
14
12
10
8
6
4
2
0
0
50
100
150
200
250
300
350
Number of days since 1st January
Figure 4. Weight of honey in one beehive in Sewanee, TN, plotted against
time.
400
41
Note that we chose to have the y-axis start at zero. This is to emphasize that the honey
supply never reached this point. It is sometimes appropriate to have a y-axis that does not
start at zero, but if you do this, make note in the title, or in a legend, that the axes do not start
at zero.
Relative proportion in forest
Bar graphs: So far we have only encountered graphs that are used with continuous or count
data. In contrast to scatterplots and linegraphs, bar graphs are most useful when our data in
divided into categories. The x-axis on a bar graph is divided into discrete categories, the y
axis is usually continuous. For example, if we had measured the relative proportions of five
tree species at one site in Sewanee we could plot the following graph:
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
White Oak
Chestnut Oak
Pignut
Hickory
Tulip Poplar
Red Maple
Species
Figure 5. Relative proportions of five tree species at one site on the
Sewanee Domain.
Bar graphs can also be used to show standard deviations. For example, if we had
repeated our measure of relative proportions at ten sites on the Sewanee Domain and found
the following means and standard deviations:
Table 3. Average and standard errors of the relative proportions of five
tree species sampled on ten plots on the University of the South Domain.
Species
White Oak
Chestnut Oak
Pignut Hickory
Tulip Poplar
Red Maple
We could draw the following bar graph:
Mean
Standard error
0.1
0.25
0.2
0.25
0.2
0.01
0.03
0.02
0.05
0.02
Relative proportion in forest
42
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
White Oak
Chestnut Oak
Pignut Hickory
Tulip Poplar
Red Maple
Species
Figure 6. Mean and standard deviation of the relative proportions of five
tree species measured at ten plots on the Sewanee domain.
ASSIGNMENT
1. The following graph is full of errors! The graph shows the mean percentage of “slow
twitch” muscle fibers in the leg muscles of various athletes. The mean was taken from
ten athletes in each category.
% Muscle vs. Sport type
90
80
70
60
50
40
30
20
10
0
Swimmer
Long-distance
runner
Sprinter
Cross-country
skier
Weight lifter
Sport
On a separate piece of paper answer the following questions:
i.
Title: What is wrong with this graph’s legend? Suggest a better title.
ii.
43
Axis labels: What is wrong with the labels on the axes? Suggest better axis labels.
iii.
Legend (the box on the right hand side): What is wrong with this graph’s legend?
iv.
Graph type: Why is this line graph inappropriate?
2. For each of the following situations, pick the most appropriate graph type (e.g., box plot,
line graph, or scatter graph). Explain your choice in each situation.
i.
You have measured the abundance of ten species of bird in ten forest plots over
10 years. You want to produce one graph that will summarize how bird populations
have changed through time.
ii.
You have calculated the average and standard deviation of the fat content of three
brands of donuts. You want to summarize this data on one graph.
iii.
You have measured the body size of one hundred adult female darters (darters are
fish that live in many Tennessee streams). You also counted the number of eggs in
each female’s ovaries. How would you present the relationship between these two
variables?
iv.
You have measured the membrane potential of an egg cell before, during and after
entry by a sperm cell. How would you show how the potential changed through
time?
45
CHAPTER 8: AN INTRODUCTION TO MICROSOFT EXCEL 2000
This tutorial will introduce you to data analysis and graphing in Microsoft Excel. You
should work through this tutorial with Excel open on a computer so that you can work the
examples as you move through the tutorial.
How to enter data in Excel
When you open Excel you should see a blank worksheet, which looks like this:
Illustration 1. Blank excel worksheet.
The worksheet consists of rows and columns of worksheet spaces, or "cells". Each row is
numbered and each column has a letter. Every cell in the worksheet can therefore be referred
to by a letter and number. For example, the top left hand cell is called A1, the cell below it is
A2, etc.
You can type either numbers or words in each cell. Imagine that over one week in July
we had collected data on the maximum daily temperature in three locations (inside Woods
Lab, on the lawn in front of the library and in Abbo's Alley). To enter this data into Excel,
point the cursor on the cell, then click to select the cell. You can now type into the cell. In
cell A1 type "Day". In cells B1, C1 and D1 type "Woods Lab", "Library Lawn" and "Abbo's
Alley". Your worksheet should look like this:
46
Illustration 2. Sample worksheet showing category titles for a new table.
Now you can enter the data into each column. Type the following numbers:
Illustration 3. Sample data table.
You can now save your work by selecting “Save” or “Save as” from the “File” menu.
You can change the appearance of your worksheet by adding lines and by putting words
or numbers in bold or italics. For example, click on cell A1 and hold the mouse button
down. Now move the mouse over to D1 and release the mouse button. Cells A1, B1, C1 and
D1 should all be selected. You can now click on the "B" button on the Formatting toolbar to
make them bold (or the "I" button for italics, or the "U" button for underline):
Illustration 4. Using the 'Bold' button on the formatting toolbar.
You will notice that by making your column headings bold you have made the words
slightly wider so that they no longer fit into their columns. You can correct this by
47
positioning the cursor so that it falls on the dividing line between the columns. The cursor
should change from a fat cross to a skinny cross with two arrows. You can now hold down
the mouse button and move the edge of the column. Alternatively, you can select your whole
worksheet, then go to the "Format" menu and choose "Column", then "Auto Fit". This will
automatically make each column just as wide as its widest cell.
To add a line underneath the column headings, select the four heading cells (as you did to
make them bold). On the Formatting toolbar select the Borders tool which looks like this:
Illustration 5. Using the 'border' button to apply borders to a table.
You can now add a line or a box around your text. Here is my data table with bold
column headings, AutoFitted widths and a double line under the headings:
Illustration 6. Sample data table showing data and all formats applied so
far (bold column headings, autofitted widths and a double line under
headings).
48
How to do simple calculations with data in Excel
In this section you will learn how to calculate averages and do simple mathematical
manipulations of your data.
Calculating averages: Imagine that we wanted to compare the mean temperature for the
three locations. We will put this information in cells B10, C10 and D10. Click on B10 then
pull down the "Insert" menu and choose "Function…". The following box should pop up:
Illustration 7. 'Paste function' box showing some function categories and
functions.
To calculate the mean click on "AVERAGE" in the right hand box, then click "OK".
[Note: to calculate the median, click on MEDIAN, to calculate the mode, click on MODE].
A new box will pop up (if the box is blocking your table you can click on the top of the box,
hold the mouse button down and drag the box to one side). In this new box you will tell the
computer which cells you wish to have averaged. The temperature data for Woods Lab are
in (B2 through B8 so type "B2:B8", then choose "OK".
49
Illustration 8. Function box for calculating the average of a range of
values.
If everything went well, you should have "20.142857" in cell B8. This is the mean of the
seven temperatures for Woods Lab. Excel makes it very easy to calculate the means for our
other columns. Click on cell B10 again, then position the cursor so that it is just above the
bottom right hand corner of the cell. The cursor will change from a fat cross to a skinny
cross. Now click the mouse button down and drag it across C10 and D10, then let go. You
should see two new numbers in C10 and D10 - these are the averages of the other two
columns. By using the skinny cross you have pasted the function "average" from cell B10
into cells C10 and D10. You can check that Excel did this correctly by clicking on C10.
Above the worksheet you will see a little window that should contain the correct formula
[=AVERAGE(C2:C8)].
The mean gives us a measure of the "center" of the data. We also want to summarize the
variability in our data. One measure of variability is the standard deviation. Variable
datasets have large standard deviations, uniform datasets have small standard deviations. To
calculate the standard deviation in Excel follow the same directions as the calculation of the
means: Click on cell B11, choose "Function" from the Insert menu and ask Excel to paste
"STDEV", then type "B2:B8" to tell Excel where to find the data. You can use the skinny
cross again to paste your formula into the adjacent cells.
If all went well you should have the following values in your table (note that I have
added labels and a line to make my worksheet clear):
Illustration 9. Output table showing values for calculations of mean and
standard deviations for temperature classes.
50
The means and standard deviations that Excel has given us go to eight decimal places, but
our data were not measured this precisely! To change the number of decimal places, select
B10, C10, D10, B11, C11 and D11 (to do this click on B10, hold the mouse button down and
move over to D11) then choose "Cells" from the "Format" menu. The following box will
pop up:
Illustration 10. 'Format cells' option box.
Click on "Number" in the left hand box, then use the scroll arrows to tell Excel to use just
one decimal place, then click "OK":
Illustration 11. Applying format of 1 decimal place to selected cells.
51
Your worksheet should now look like this:
Illustration 12. Sample data table showing all formatting and
calculations so far.
To print this table, select the area you wish to print (cells A1 through D11), then go to the
File menu, choose "Print Area" and "Set print area". You can now print what you have in the
selected region.
Now you will use Excel to create a new column using a simple mathematical
manipulation of the other columns. Suppose that we wanted to know what the temperature
difference was between the inside of Woods Lab and the Library lawn. Click on cell F2,
then type "=C2-B2" into cell F2, then before pressing Return (or Enter) move the cursor to
the bottom right corner of the cell until you see the skinny cross. Now click on this corner
and pull down until you reach cell F8. When you let go of the mouse Excel will have
calculated the difference between column C and column B for each row.
Again, you should add a column heading to make your table clear:
Illustration 13. Data table with heading for new column.
Using the Fill function
Excel can do many other types of mathematical manipulations of your data. Here is one
more example: Suppose that we were studying some fish in a lake. The initial size of the fish
52
populations is 15 and the fish population doubles each year. We will use Excel to calculate
the size of the population after 10 years.
In cell A13 type "Year", in cell B13 type "Population size". Now type "1" in cell A14
and "15" in cell B14. You are now ready to make Excel do some work for you -- type
"=A14+1" in cell A15, then press return. The number "2" should appear in cell A15. Now
position the cursor over the bottom right corner of the cell until you see the skinny cross.
Now click on this corner and pull down until you reach cell A23. When you let go of the
mouse Excel will have produced a series of years from 1 to 10 by adding one to the cell
above every cell in the column. Now click on cell B15 and type "=B14*2", then press return.
B15 should now have "30". Click on B15 and position the cursor over the bottom right
corner of the cell until you see the skinny cross. Now click on this corner and pull down
until you reach cell B23. What is the population size of fish in year 10? You should have
7680.
How to draw graphs in Excel
Excel allows you to draw many different types of graph. Unfortunately, Excel is not
smart enough to know which graph is the most appropriate for the type of data you have, so
beware! Think about what kind of graph you draw before you start clicking away in Excel.
You will now make a line graph of the temperature in the three locations throughout the
week. First, select cells A1 through D8. This will highlight all the data and the column
headings. Now go to the top of the screen and click on the "Chart Wizard":
Illustration 14. 'Chart wizard' button on 'Standard' toolbar.
A new box will pop up and give you some options. You will choose the XY scatter
option on the left and the "connected data points" option on the right. Note: even though you
are drawing a linegraph do NOT use the "Line" option!
53
Illustration 15. 'Chart type' selection box in 'Chart wizard' function of
Excel.
Click on "Next". The next box allows you to tell Excel where to find the data for the
graphs. We have already selected our columns, so you can just click "Next" again. In the
next box you will add axis labels, graph titles, legends, etc. Normally each axis on a graph
should include a label with units. Type "Temperature (Degrees Centigrade)" for the y axis
and "Day" for the x axis. Graph legends should explain exactly what the graph is showing
and should include a figure number: "Figure 1. Graph of the maximum daily temperature
during one week in July for three locations in Sewanee" would be appropriate for this graph.
54
Figure 1. Graph of the maximum
Figure 1. Graph of the maximum daily
temperature during one week in July for three
locations in Sewanee.
Illustration 16. 'Chart wizard' window showing options for chart
formatting.
Before clicking "Next", click on some of the other options to see how you can modify
your graph (e.g., remove gridlines, change the location of the legend).
Now press "Next" to go on to the last box. This box allows you to tell Excel where to put
your new graph. You can choose "As an object" to have the graph appear alongside your
data table, or "As a new sheet" to add the graph as another "sheet" in Excel. Using "sheets"
allows you to keep your file tidy.
Illustration 17. 'Chart wizard' chart location window.
55
You can switch between sheets using the tabs at the bottom of the screen (click on the
tabs to move between sheets):
Illustration 18. 'Page tabs' allowing you to move between pages of your
'work book'.
You should now have a nice line graph of your data.
If you want to change your graph just double-click on the part that you wish to change.
For example, double-click on the y axis label to change the label text or font, double-click on
the numbers on the y axis to change the range, double-click on the points to change their size
or the thickness of the line.
Now you will draw a scatter plot of the temperature in Abbo's Alley plotted against the
temperature outside the library. To do this, first select cells C1 through D8:
Illustration 19. Selecting cells to make a scatter plot.
Now click on the chart wizard and select XY Scatter with no line:
Illustration 20. Selecting xy (scatter) in chart wizard.
56
The rest of the process is very similar to our first graph. You should end up with a graph
similar to the following:
Figure 1. Graph of the temperature at Abbo’s Alley
plotted against the temperature at the library lawn for
seven days in July.
Figure 7. Example of what your scatter graph should look like.
The last type of graph that we will explore in this tutorial is the bar graph. We will plot
a graph of the average temperature at the three locations. First select the cells that can
contain the average temperatures:
Illustration 21. Selecting cells to make a bar graph.
Now click on the chart wizard and select "Column":
Illustration 22. Selecting 'column' chart type and first subtype in 'chart
wizard'.
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In the next box leave the "Data range" as it appears (you selected your data, so Excel
knows where to find it) and click on "Series":
Illustration 23. Selecting 'series' tab in chart wizard.
You will now tell Excel where to get the labels for the x axis. Click in the X axis labels
box:
Illustration 24. Selecting location of axis labels.
Now click on cell B1 in your worksheet and hold the mouse down. Move the mouse over
the cell D1. These will give Excel the labels for the x axis. Don't panic if part of your graph
wizard disappears for a moment! You should see the following:
Illustration 25. Location and range values for cells selected for axis
labels.
Click "next" to go on and add a title and axes labels to your graph. It should look like
this when you are finished:
58
Figure 2. Graph of mean temperatures during one week
in July at three locations in Sewanee.
Figure 8. Example of properly formatted bar graph.
You have one more step: adding symbols to your graph to indicate the standard
deviations. You cannot do this in the chart wizard, only afterwards. Note that you can use
the same sequence of clicks to put standard deviations on other graphs such as linegraphs.
Click on your graph so that a dot appears in each column (if this doesn't happen, try clicking
somewhere else on your sheet or graph, then clicking on the columns again):
Illustration 26. View of bar graph with columns selected.
Now double-click one of the columns. You will get a box with several options; choose the
"Y error bars" option:
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Illustration 27. Options available for column formatting.
Now click on the "Both" box and the "Custom" box, then click in the box to the right of
the "+" sign, then click on cell B11 in your worksheet and hold the mouse down. Move the
mouse over the cell D!1. These will give Excel the values for standard deviation. Don't
panic if part of your graph wizard disappears for a moment! Repeat for the "-" box, then
recheck to make sure that "Both" is selected at the top of the options box. You should see the
following:
Illustration 28. Error bar formatting window.
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Your completed graph should look like this:
Figure 2. Graph of means and standard errors for
temperatures during one week in July at three
locations in Sewanee
35.0
30.0
25.0
20.0
15.0
10.0
5.0
0.0
Woods Lab
Library Lawn
Abbo's Alley
Location
Figure 9. Completed bar graph with proper formatting, labeling and
error bars.
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ASSIGNMENT:
The data for this assignment are found on the Angelnet file server in an Excel 2000
workbook called "Sample data for Intro Bio". You should copy this file onto a disk and work
through the following problems. To navigate to this file, choose the appropriate directions
below.
Mac-- Open the chooser from the Apple menu on the computer. You should see something
like this:
Illustration 29. View of the 'chooser' window where the AngelNet file
server can be accessed.
Click on AppleShare in the top left box, then Sewanee Net Servers in the bottom left box,
then 00-AngelNetFileServer in the top right, then click OK. A box will pop up - click on the
"Guest" button. The following will come up:
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Illustration 30. Menu of items found on the AngelNet file server.
Click on "Acad_Classes" then click OK. The following icon should appear on the
desktop of your computer:
Illustration 31. 'Academic classes' icon that will appear on your
computers desktop.
Double-click on this, then open the following folders: Sciences Math/Biology/Intro
Bio/Stats. The file "Sample data for Intro Bio" is in this folder. To make a copy of this file,
drag it into a folder on a disk.
Now disconnect from the Academic Classes on Angelnet fileserver by dragging its icon
into the trash.
PC—Open the ‘Start’ menu at the bottom left corner of your desktop with a single click,
choose ‘Run’ from that pop up menu. In the ‘Run’ dialog box type
\\angelnet_server\Acad_Classes then press ‘OK’. A window titled ‘Acad_classes on
angelnet_server’ will appear on your desktop with several folders inside. Navigate through
the following folders: Sciences Math/Biology/Intro Bio/Stats. The file "Sample data for Intro
Bio" is in this folder. To make a copy of this file, drag it into a folder on a disk.
Now disconnect from the Academic Classes on Angelnet fileserver by clicking on the ‘X’
in the upper right corner of the window or chose ‘Close’ from the ‘File’ pulldown menu.
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Open the file ‘Sample data for Intro Bio’ in Excel and answer the following questions on
a separate sheet of paper:
1. The “Farms” column has data about the size of dairy cow herds on farms in Tennessee.
Each number represents the number of cows on a farm. There are 30 farms in all.
i.
What is the mean and median number of cows on each farm?
ii.
Why do the mean and median differ?
iii.
Would you report the median, mode or mean, if you wanted half the farms in
the sample to be above the value and half below?
2. The "Volume" and "Weight" columns contain data about the volume and weight of 40
hickory nuts that were collected in the fall of 1998 in a cove forest in Sewanee. Draw an
appropriate graph to show whether volume and weight of these hickory nuts are
correlated with each other. Make sure you fully label your graph, then print it out.
3. The next table shows data about performance scores for five pigeons. These birds were
trained to perform a complex series of pecks with their beaks in order to receive a reward.
Their "score" summarizes their performance in this task. Scores are shown for each
pigeon on each of ten days. There are two blank columns: mean and standard deviation.
You should use Excel to calculate these means and standard deviations, then plot a
suitable graph to show how the scores changed through time. Include the standard
deviations on your graph, Print out this graph.
4. The "Year" and "Human Population" table shows the estimated human population over
the last 1000 years. Plot a suitable graph to show this data. How long does it take for the
human population to double? If past trends continue, what year will the populations hit
12 billion?
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CHAPTER 9: USING EXCEL TO PLOT DATA (BIOLOGY 132)
A. GETTING SET UP
•
•
•
•
•
•
Turn on computers
Login (should be same as email login)
Find Excel and open it
Under the Help menu, make sure that the Microsoft Assistant (that annoying
animated paper clip) is turned Off. If there is an option that says “Turn Assistant
Off”, scroll down and highlight this. If the option reads “Turn Assistant On” then it is
already turned off.
Under the View menu > Toolbars, make sure that the Standard and Formatting
toolbars have a check (√) beside them so that they will be displayed at the top of the
screen.
Under the View menu make sure that the Formula Bar has a check beside it so that
it will be displayed.
A blank page should appear on your screen
• this is a worksheet where you can enter your data
• each rectangle in the worksheet is called a cell
• to enter something in a cell, simply highlight the cell (click on it) and begin typing
• use tab key to move across the table, return key to move down the table, mouse to
click on a cell and move to it. You can also use the arrow keys to navigate among
cells
B. ENTERING YOUR DATA
•
•
•
•
we will begin by entering your spectrophotometry data from last week
set up a data table like you did in your lab notebook, with one column headed
Wavelength (nm), and the second column headed Absorbance
enter your data 420,440, 460 etc. down to the final wavelength used, and include
the measurements you made every 5 nm at the end, hitting return after each entry
to advance down to the next cell in the column. Do the same for the
corresponding absorbance readings (e.g. 0.148, 0.179. 0.210 …)
Remember: you can always UNDO any changes you made by using the Edit >
Undo command (CTRL Z on PCs, or ‘apple’ Z on Macs)
C. SAVING YOUR WORK
•
•
File>Save> “filename”
before pressing ok, designate where you want to save it on the computer. By default
it usually puts it in My Documents, or something like this. To make it easy to find,
save it to the Desktop. You can always move it later if you want to.
D. FORMATTING YOUR DATA
•
Note that you will need to SORT your data in order of increasing time to get Excel to
graph it properly. To do this, highlight all of the values in both columns, and from
•
•
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the menu at the top select Data > Sort.... From the pop-up menu, choose the column
you wish to use as your sorting category and whether you want values in ascending or
descending order (choose ascending). Click OK and check to see that the entire area
you wanted sorted is highlighted, and that things have sorted the way you want.
use the Formatting toolbar to format text to your liking (e.g. centering text, or
right/left justify text in columns, changing font size, bolding/underlining text, adding
borders)
adjust column width and row height by placing the cursor between at the end of the
column or row you wish to adjust, hold down the mouse button and drag to the
desired size. To automatically resize a column or row, place the cursor at the end of
the row or column and double-click on the separator.
E. GRAPHING YOUR DATA
By default, Excel graphs data by using the left-hand column for the X axis, and the righthand column for the Y axis. Setting up your tables with this in mind makes it relatively easy
to produce a chart (= graph = figure) in Excel.
• using your wavelength/absorbance data, highlight the complete data set, including the
column headings (Wavelength , Absorbance). To do this, click on the top left cell
(Wavelength), hold the mouse button down and dragging it over the entire area, to the
bottom right corner.
• release the mouse button. The entire data set should now be highlighted.
• click on the Chart Wizard icon in the Standard Toolbar and a menu will appear
• STEP 1 - CHART TYPE: pick the type of graph you want to make. If you are not
sure, select one and click on the Press and hold to view sample button to preview
the graph. You can use this feature to explore different ways of presenting your data.
• what type of data do you have? Column, Bar and Line charts use discrete data
(categories or counts) on the X-axis and continuous data on the Y-axis; Scatterplots
use continuous data on both the X- and Y-axes.
• once you select graph type, Excel will take you through a series of steps in building
the graph. You can go back or bail out at any point. After you have pressed Finish,
you can still edit the graph.
• STEP 2 - CHART SOURCE DATA: if you highlighted your complete data table
before you hit the Chart Wizard tool, all should be fine. If not, you can make
adjustments here.
• STEP 3- CHART OPTIONS:
o Titles:
ƒ Chart title: Use this for your Figure title: E.g. Figure 1.
Spectrophotometric determination of … (you fill in the rest)
ƒ Value (X) axis: enter the independent variable name (and units if
applicable)
ƒ Value (Y) axis: enter the dependent variable name (and units if
applicable)
o Axes:
ƒ both X and Y value boxes should be checked
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•
•
•
o Gridlines:
ƒ a check in a box displays the gridlines noted. Default is Y-axis major
gridlines on. Some prefer to turn these off.
ƒ turning on X- and Y-axis major gridlines will give the appearance of
graph paper, which can be useful if you are trying to use the graph to
scale off values.
o Legend:
ƒ Default is “Show Legend” This is only necessary if you have more
than one dependent variable. For the first graph today, turn it off, for
the second graph, you will need to have it on.
o Data labels:
ƒ Default is none – keep it like that
STEP 4- CHART LOCATION: here you have the option of placing the chart on the
same page as your data (as object in the current worksheet – this is the default
option), or placing the chart on a separate sheet.
for starters, place the chart on a separate sheet (you can always move it later). This
way you get a full page blowup of your chart and it is easier to format
It helps to name the sheet so that you can find it easily later (e.g. change Chart 1 to an
informative name – e.g. WavMaxChart). The name will appear on the tab at the
bottom of the worksheet.
When you click FINISH the chart will appear where you asked to place it. Now you can
continue to format the chart.
F. FORMATTING YOUR CHART
Double-clicking on many areas of the chart will bring up a menu that gives you various
options. For example
Getting rid of the gray background on the chart (it only wastes toner or ink)
• double click on the background of the graph (somewhere within the axes) to change
the background color and plot frame (Default is gray with a gray border). Choose
Patterns > Border > None, and Patterns > Area > None
• the same menu can be obtained by a single click on graph background, then going to
the Format menu at the top > selected plot area
Rescaling X- or Y- axis
• double click on the axis of interest
• enter the most appropriate minimum and maximum values and interval units (e.g.
Wavelength 400-700 nm, interval scale will depend on the size of your graph)
Editing and moving chart titles and axis labels
• single-click on the label of interest and a box will appear around it. You can then
drag the box to move it to a new location. ALWAYS MOVE YOUR FIGURE
TITLES TO THE BOTTOM OF THE FIGURE
• after the box appears, a second single-click will allow you to edit the text in the box
68
•
•
•
double-click on the label to bring up a menu box that allows you to change font size,
color, etc.
selected text can also be reformatted using the formatting toolbar
To change font or font size for the entire chart, click in upper left of chart (or
anywhere outside of the axes). A black square appears around the chart to indicate
that the entire chart is selected. Then use the formatting toolbar to select the desired
font features that will be applied to the entire chart (alternatively, go to the Format >
selected chart area > Font tab from the menu bar at the top of the screen)
Edit Chart features using the Chart menu from the top of the screen
• the menus you saw when you were first constructing your chart are found under the
Chart menu at the top of the screen. This tab is only visible when the active cell
selected by your mouse is within the chart area (i.e. you won’t see it if your cursor is
in a data cell of a worksheet). From these you can change your chart type, add new
series of data, edit chart features, and move the chart to a different location.
SAVE YOUR WORK!
G. PREVIEWING AND PRINTING YOUR CHARTS
•
•
•
preview your chart before printing by selecting File > Print Preview. The chart will
only appear if your cursor has selected the chart or sheet as the active area
In page setup (or the Setup button from the Print Preview screen), you can change the
page orientation (landscape vs. portrait), set the page margins, add headers or footers
(usually not necessary here since your figure will have a title) and define how you
want your Chart to be sized.
when you are satisfied with your print preview, select PRINT
H. MORE THAN ONE DEPENDENT VARIABLE – PLOTTING A DOUBLE Y-AXIS
In your second experiment, in which you measured absorbance and % transmittance for each
of five different dilutions of neutral red, you have 2 dependent variables (absorbance and %
transmittance)
• enter and save your data into a new Excel workbook
• This time, you should have 3 columns of data:
Concentration of neutral red (mg/ml), Absorbance, % Transmittance
•
•
•
•
highlight all the data, and select the chart icon
Select chart type (continuous data, therefore scatter plot)
go through steps until you get the chart
place the chart on its new sheet
Notice: both variables appear, but you can barely see the absorbance data. This is because
they are both plotted on the same Y axis, where the scale runs (at present) from 0-120. It
69
would be much more useful if the absorbance was on a scale from 0-1 (or 0-just above your
max. absorbance reading) and % transmittance was on a scale of 0-100%.
To put the two variables on separate axes, but within the same graph, one variable needs to
go on a ‘secondary y axis’, which will be labeled on the right-hand side of the graph.
•
•
•
•
•
•
•
•
•
To change the axis for one of the variables, double click anywhere on the data line for
that variable (% Transmittance is easier since it is more accessible).
a menu appears that has several tabs. Click on the ‘axis’ tab. It is telling you that for
the line you selected (%T) it is being plotted on the primary (left hand side) axis.
click on secondary axis to shift %T over to the secondary axis.
note in the graph preview that the absorbance now covers almost the entire height of
the graph. This is because the primary y axis has been rescaled automatically to best
fit the absorbance data.
click okay and have a look at your newly formatted graph.
rescale the y axes to get absorbance 0-1 (or whatever is most appropriate given your
absorbance readings), and %T 0-100 %
do any other formatting changes that you want. MAKE SURE YOU GIVE YOUR
FIGURE AN APPROPRIATE TITLE
preview your chart
check with your lab instructor before printing your work
I. FINDING THE SLOPE OF THE LINEAR PORTION OF THE CURVE
In the enzyme exercises and your enzyme experiment, you will need to determine the slope
of the best fit line through the linear portion of your data. This will give you the rate of
enzyme activity. You will first graph all your data and print it. Use that graph to determine
which part looks the most linear. Here’s how you get the rate over the linear part of the
curve.
• select only the cells in all columns corresponding to the range of times where the data
appear linear and copy them.
• click on your graph anywhere
• from the Edit menu choose Paste Special and within that click on Categories (X
values) in First Column and then click OK.
• click on a data point in the linear portion of the graph (should be a different color
from the other points)
• from the Chart menu choose Add Trendline
• click on the Type tab
• click on Linear
• under Based on Series in that same window, choose the series for the trendline
• click on the Options tab
• place check marks in Display Equation on Chart
• click OK
• repeat from Add Trendline until you have trendlines for each of your reactions
• the formula for the line is y = mx + b and so “m” is the slope = rate of reaction
71
CHAPTER 10: INFERENTIAL STATISTICS OR "STATISTICAL
TESTS"
We use inferential statistics when we want to go further than simply summarizing our
data. For example: (i) we might want to use some data about trends in the abundance of an
endangered species to predict whether the species will decline or whether it might increase,
(ii) we might want to test whether one brand of insect repellent is more effective than
another.
These two examples illustrate the two main types of statistical test: those that look for a
difference between two groups of numbers (e.g., the number of insects biting your arm) and
those that look for an association or correlation between groups of numbers (e.g., the
abundance of an endangered species and time).
Why use statistics when we can just show our data on a graph? Statistics allow us to
make an informed decision about whether the patterns we see in our data are due to chance or
not. For example, if we toss a coin 10 times and get 6 heads and 4 tails, we would not be
inclined to suggest that the coin was “unfair”, whereas if we got 10 heads and no tails, we
might be suspicious. How about an 8:2 ratio, or a 7:3? Statistics provides rules to help us
decide in these borderline cases (and most data are “borderline”, at least in the eyes of
reviewers of scientific manuscripts!).
Statistical tests will not provide definitive answers - for example, tossing a coin and
getting twenty heads in a row indicates that the coin might be double-headed, but no
statistical test can tell us for sure. What statistical tests can do is provide us with a
quantitative evaluation of the probability that the patterns we see in our data are due to
chance alone.
For example, a statistical test on our coin-tossing data would tell us that if we sat down
and tossed a coin 20 times, then repeated this a million times, that on average only one of the
million trials would give us 20 heads in a row. We would say that the p-value (p for
“probability”) for getting 20 heads in a row is about p = 0.0000001. Thus, the output of most
statistical tests will be a probability usually called the "p-value". The p-value is a measure of
the probability that the pattern we see in our data is due to chance alone.
That was a pretty extreme example. Most of our data give us much larger p-values. For
example, suppose we conducted a statistical test to see whether a random sample of Dunkin’
Donuts had more fat in them than a random sample of Mister Donuts (we’ll learn how to
actually do these tests in a moment). The mean fat content for Dunkin’ Donuts was 5.5
grams and 4.9 grams for Mister Donut; the statistical test gives us a p-value of p=0.02. What
does this mean? The p value tells us: "If Dunkin’ Donuts and Mister Donut did not differ in
their fat content we would expect samples from each of them to differ by this amount (0.6
grams) in just 2% of cases".
Is a 2% chance (i.e. p = 0.02) small enough to conclude that Dunkin’ Donuts are more
fatty?
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In general, if the p-value is less than 0.05, we conclude that we
have found a “statistically significant” result.
NOTE: This 0.05 (or 5%, or 1 in 20) is an arbitrary cutoff point. If the cost of making
an incorrect decision was very high, we might want to chose a cutoff off 0.01 or even 0.001.
So that explains the magical p-value. We’ll see now how these p-values fit in with
scientific hypothesis-testing:
Two fundamental ideas in science and in statistics are the null hypothesis and the
alternative hypothesis. For any statistical test we should specify both. The null hypothesis
(sometimes referred to as Ho) describes the situation where there is no difference or
association within our data. The alternative hypothesis (sometimes referred to as HA) is that
there is some difference or association in our data. For example, if we were to compare the
length of beaks on male and female downy woodpeckers Ho and HA would be as follows:
Ho: Male and female downy woodpeckers have the same length beaks.
HA: Male and female downy woodpeckers have beaks of different lengths.
Alternatively, if we conducted a more detailed statistical test we might state multiple
alternatives:
Ho: Male and female downy woodpeckers have the same length beaks.
HA1: Male downy woodpeckers have longer beaks than female downy woodpeckers.
HA2: Female downy woodpeckers have longer beaks than male downy woodpeckers.
We can now rephrase our understanding of the p-value in terms of null and alternative
hypotheses. First, we acknowledge that even if the null hypothesis is true, chance alone will
sometimes produce data samples that contain some pattern in them (e.g., tossing a coin and
getting 20 heads in a row). The p-value is the probability of finding this pattern in our data if
the null hypothesis is true.
Let’s revisit the donut example and state it in terms of null and alternative hypotheses:
Ho: Dunkin’ Donuts and Mister Donut have the same fat content.
HA1: Dunkin’ Donuts has a higher fat content than Mister Donut.
HA2: Dunkin’ Donuts has a lower fat content than Mister Donut.
We would state our conclusion as follows:
“We reject the null hypothesis and accept the alternative hypothesis (at the p=0.02 level)
that Dunkin’ Donuts has a higher fat content than Mister Donut.”
If the p-value had been p=0.13, we would state:
“We do not reject the null hypothesis (p=0.13) that Dunkin’ Donuts and Mister Donut
have the same fat content”.
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NOTE:
◊ Always either reject or do not reject the null hypothesis. Never reject or fail to reject the
alternative.
◊ Always report your p-value when making statements about rejecting or not rejecting the null.
◊ You can never prove the alternative hypothesis. At best you can “accept” or “support” it at a
given p-value.
If you are at all familiar with Karl Popper’s views on how science should be conducted
you will see some strong parallels here: Science moves along by falsifying hypotheses (i.e.
rejecting null hypotheses), and nothing is ever proven in science (i.e. we never prove the
alternative hypothesis).
We'll now explore two different statistical tests and you'll learn how to do these tests in
Excel 2000. First you need to tell Excel 2000 to load up a package of "add ins" that will
allow it to conduct statistical tests. To do this, go to the "Tools" menu and select "Add-Ins".
A box should pop up - click on "Analysis Tool pack" so that a check mark appears to its left
then click OK (if the check mark is already there you can just click OK).
The following examples will use data that is in the file "Stats examples for Intro Bio" that
is in the same folder on the FileServer as the sample data you used for your previous
worksheet. You should copy this file onto a disk, and follow along See the worksheet at the
end of Chapter 9 in your Survival Guide for detailed instructions to retrieve the data file.
Launch Microsoft Excel and open the file ‘Stats examples for Intro Bio’.
The t-test:
Use a t-test to compare the means of two samples. In order to use this test your data
should be continuous, but sometimes people use this test with count data (i.e. integers).
We will use the first two columns for our t-test. These columns show data on the average
width of the annual growth rings from chestnut oaks growing on the bluff (i.e. on the very
edge of the cliff) and on the interior of the Plateau. Each number represents data taken from
one tree, so 30 trees were sampled in each group. We want to know whether the means of
these two groups of numbers differ significantly from one another so we will use a t-test.
Thus, our null and alternative hypotheses are:
Ho: Bluff and interior plateau chestnut oaks have the same mean width of their annual
growth bars.
HA: Bluff and interior plateau chestnut oaks differ in the mean width of their annual
growth bars.
Go to the "Tools" menu and select "Data analysis", then choose "t-test: Two-sample
assuming equal variances" as shown below:
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Illustration 32. Data analysis 'tools' window.
*** If 'Data Analysis' does not appear under your 'Tools' menu then do the following:
Under 'Tools' choose 'Add-Ins', check (√) 'Analysis Tool-Pack' then select OK. When
your computer stops buzzing you should be able to find 'Data Analysis' on the 'Tools'
pull-down menu.
Excel 2000 will now ask you to fill in several boxes. For the "Variable 1 range" you
should click in the box to the right of the text, then select all the numbers in the "Bluff"
column:
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Illustration 33. T-test box and data sheet, showing selection of cells for
variable ranges.
Now click in the Variable 2 range box and select all the numbers in the Plateau column.
For "Hypothesized mean difference" type in 0 (zero). We want to test whether the means
differ from each other, so our null hypothesis is that the means are the same.
For "Alpha" type in 0.05. This is the cutoff point for our p-value - if it is below 0.05 we
will conclude that there is a significant difference between the means.
Last, click on "New worksheet ply" and type in a name for this sheet. This will put your
results on a new sheet in your Excel 2000 workbook.
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When you are all done your box should look like this:
Illustration 34. T-test box with variable ranges selected.
Press OK and Excel 2000 will conduct the test for you. Your output will look like this:
Illustration 35. Output table following t-test.
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What does all this mean? The most important row in the table is:
"P(T<=t) two-tail". This gives the p-value for this test. Here p = 0.0032
The other rows also have some interesting data: The first row shows the mean value of
your two data columns. Thus, the Bluff data have a mean of 0.406, the Plateau data have a
mean of 0.617. The "Variance" row gives the variance which is a measure of the amount of
variation in the data (variance = (standard deviation)2). The "Observations" row shows the
sample size in each sample. The "df" row gives a number called the "degrees of freedom".
This number is used in the statistical test as a measure of the sample size. For technical
reasons, df is always lower than the actual sample size.
That is a lot of information. How should you report this statistical test in your lab
reports? Here is one way:
A t-test indicated that there was a significant difference between the means of the two
groups (p = 0.0032, df = 58). These data therefore support the hypothesis that Chestnut
oaks growing on the Bluff have narrower growth rings (mean = 0.404 mm) than Chestnut
oaks that grow on the interior Plateau (mean = 0.616 mm).
Note that:
◊
You should always give the p-value and the degrees of freedom.
◊
You never prove your hypothesis - you can just support it.
◊
You should give some indication about the magnitude of the effect you have studied (in
this case we reported the actual means). If you have a large enough sample size you can
detect very, very small differences between groups. These differences might not be
biologically meaningful.
Linear regression
Use these if you want to use one set of numbers to look for an association between two
groups of numbers or when you want to use one set of numbers to predict the value of
another.
Only use this test if a scatter plot of your data suggest that there might be a linear
relationship. Here are some examples. ONLY graphs A and D suggest a linear relationship
(although graph D might also be an S-shaped relationship).
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A
B
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*
*
*
*
*
*
C
*
*
*
D
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
Illustration 36. Four scatter graphs illustrating potentially linear and
non-linear data. Graphs A and D suggest potential linearity, while
graphs B and C do not.
Linear regression uses one variable (=independent variable, x axis) to predict the value of
another (=dependent variable, y axis). The regression will calculate a “best fit” line through
your data. This calculation involves minimizing the square of the distance between the line
and each point (you don’t need to know the details of this calculation) - the line is therefore
called the “least squares” line. NOTE: statistics software packages will calculate a linear
regression through anything regardless of whether the data actually are linear. If your data
are linear, the regression line allows you to predict the value of the variable on the y-axis
from the value of the variable on the x-axis. If your data are not linear, the best fit line will
give you garbage!
The example for linear regression is also in the Stats Examples file. The data show
measurements taken from 30 cardinal flowers (these are plants that grow throughout
Sewanee - they are found in wet areas and have bright red flowers that are pollinated by
hummingbirds). We have measured the mass of all the roots and the number of seeds
produced by each plant. We want to know whether root mass can predict the number of
seeds produced. Thus, our null and alternative hypotheses are:
79
Ho: The root mass of cardinal flowers does not predict the number of seeds produced by
each plant.
HA: The root mass of cardinal flowers predicts the number of seeds produced by each
plant. [Note that this alternative hypothesis does not state whether there will be a
positive or a negative relationship between root mass and number of seeds. We could
write two alternative hypotheses: one for a positive relationship, one for a negative
relationship]
To conduct a regression: Select "Data Analysis" from the tools menu. Then choose
"Regression". Fill in the Data Range boxes by clicking in them, then selecting the "Root
mass" data for the x-axis and "Number of seeds" data from the y-axis. Type in a name for
the new sheet for your results, then press OK.
Illustration 37. Regression box, showing cells selected for x and y input
ranges.
Excel will then show you three tables that summarize the results of the regression. The
p-value is given in the second table in the box at the far right hand (under "Significance F").
In this case p = 0.426. This is greater than 0.05, so we have not rejected the null hypothesis.
The degrees of freedom are given in the second column of the second table. In this case
we have 28 degrees of freedom.
We could report these data by writing:
A linear regression indicated that the root mass of cardinal flowers did not predict the
number of seeds produced by each plant (p = 0.426, df = 28). These data therefore fail to
reject the null hypothesis that there is no relationship between root mass and number of
seeds produced.
80
The tables also give some other interesting information. The linear regression calculates
the equation of the best straight line through the data. This equation has the form:
y=mx+c
where m is the slope of the line and c is the intercept on the y axis
In this case, the intercept is 5.806 and the slope is -0.145 (these values are in the first
column of the last table).
We can get Excel 2000 to draw a scatterplot with this best fit line as follows:
¾ --Draw a scatterplot (remember to label all axes and give it a title)
¾ --When you have finished the scatterplot go to the "Chart" menu and select "Add
trendline":
Illustration 38. Trend line options found in the chart menu.
¾ Select "Linear", then click OK. Excel will draw a best fit line for you:
Figure 1. The number of seeds produced by cardinal
flowers plotted against the root mass of each
plant.
12
10
8
6
4
2
0
0.00
2.00
4.00
6.00
8.00
Root mass (grams)
Illustration 39. Completed scatter plot with best fit line.
10.00
12.00
81
Unfortunately there is a slight discrepancy between the formula used by Excel to draw
this line and the formula used in the regression, so the equations for each are not exactly the
same. They are very close, however. The equation from the regression table is the more
accurate one.
ASSIGNMENT:
Answer the following questions on a separate piece of paper.
1. I have measured the mass of 5 male trout and 5 female trout. Is a t-test an appropriate
way of evaluating the hypothesis that female trout have higher mass than do male trout?
Explain why or why not.
2. I have conducted a linear regression of the body mass of rattlesnakes against their body
temperature. My regression gave me a p-value of 0.04 and I had 50 rattlesnakes in my
sample. Write down a null and alternative hypothesis for this regression and explain what
this p-value means.
3. The "Stats examples for Intro Bio" has data on the concentration of sodium ions in
samples of water taken from two rivers. We want to compare the means of these two groups
of numbers. Write down a null and alternative hypothesis, conduct an appropriate statistical
test and report for results as you would in the results section of a lab write up. You do not
need to include a graph for this question.
4. The "Stats examples for Intro Bio" also has data on the area of individual leaves from a
magnolia tree and the number of insects found on each leaf (i.e. each leaf was plucked from
the tree, had its area measured and all the insects on each leaf were counted). We want to
know whether leaf area can be used to predict the number of insects on each leaf. Write
down a null and alternative hypothesis, conduct an appropriate statistical test and report for
results as you would in the results section of a lab write up. Include a graph with your
results.
83
CHAPTER 11: USE OF OXFORD® DIGITAL DIAL PIPETS
Carefully following these steps will repay you with better results in your work.
1. Set the volume by turning the adjusting knob that is the top of the plunger. Do not turn
the knob to beyond the maximum or minimum ranges listed on the top of the pipet.
2. Put a disposable plastic tip on the end of the pipet by inserting the bottom of the pipet
into a tip in a rack. Do not use your hands to remove a tip and then put it on the pipet.
3. Secure the tip better by grabbing the top of the tip and pushing it on tighter while
twisting. Do not touch the tip more than 2 cm from the top - if you do, you could
introduce contamination into your solutions.
4. Push the plunger of the pipet down to the first stop position before putting the tip into
the desired solution. If you were to insert the tip into the solution before doing this,
you would blow bubbles out of the tip, and a small bubble of air would remain at the
tip, preventing you from pipeting accurately.
5. Insert the bottom of the plastic tip just below (approx. 1 mm below) the surface of the
solution you wish to withdraw, while holding the plunger at the first stop. The best
way for a right-handed person to pipet is to hold the tube or bottle containing the
solution with the left hand at eye level 4 to 6 inches from the face, so that the fluid can
be seen clearly. Hold the pipet with the right hand. This procedure can even be used if
you are making solutions on ice. Simply pick the tube up from the ice by holding it at
the top (so that you are not heating the solution at the bottom much) and keep it off the
ice as briefly as possible.
6. While holding the tip just below the surface of the solution, release the plunger slowly.
Don't just let the plunger snap up. (If you were to allow the plunger to snap up the
pipet will not draw the proper volume of fluid. This is critical for accurate pipeting of
all solutions.)
7. Remove the tip from the solution.
8. While holding the tube that will receive the solution at eye level, put the tip of the
pipetman into the recipient tube.
9. If the tube is empty, place the bottom of the tip along the side of the tube near the
bottom. If the tube has some other solution in it already and you want to mix the
solution being delivered, put the bottom of the tip just below the surface of the
resident solution.
10. To eject the solution, press the plunger down. You will notice that when you reach the
first stop on the plunger there will still be a small amount of solution in the tip. To
expel this last amount, depress the plunger fully.
11. To prevent solution from re-entering the tip, you must remove the tip from the
solution or away from the side of the tube while still fully depressing the plunger. If
you do not do this, you will make the very common mistake of sucking solution up
into your tip even well past the set volume.
84
12. Discard the tip by pressing the tip ejection button. Tips should be placed into the
regular trash baskets. You may wish to collect used tips at your bench instead of
running to the trash with each tip, but be sure to use a fresh tip for every operation.
Practice Settings
We have three sizes of digital dial pipets, each with color-coded dials on top: 0.5-10 µl
(white), 10-100µl (yellow), and 100-1000µl (blue). They are referred to, respectively, as: P10, P-100, and P-1000. The color-coded dials indicate the size and color of protective plastic
tips to be used with each. Volumes are dialed in using the colored knobs while reading the
four digit setting in the window. If a window has a horizontal line through it, indicated in the
window boxes below by a bolded line, you are to read a decimal at that line.
NOTE: Pipets should never be dialed above or below the volumes indicated on the
pipet.
P-1000: to set 950 µl
(has no decimal indicating line)
0
1000's of µl
9
100's of µl
5
10's of µl
0
1’s of µl
P-100: to set 75 µl
0
100's of µl
7
10's of µl
5
1's of µl
0
0.1’s of µl
P-10: to set 10.5 µl
1
10's of µl
0
1's of µl
5
0.1's of µl
0
0.01's of µl
85
Dial and Plunger
Button
Volume Setting Lock
Tip Ejector Button
0
Volume Setting
Window
1
2
5
Filter
Figure 10. Representation of an Oxford® digital dial pipet.
87
CHAPTER 12: SPECTROPHOTOMETRY
Many biological materials are colored (e.g., chlorophyll, melanin) or may be rendered
colored by reaction with specific dyes (e.g., the Feulgen reaction for DNA). This fact enables
biologists both to identify and quantitate such substances relatively easily.
Identification is accomplished, or aided, by obtaining an absorption spectrum of the
molecules in solution. This is possible because colored solutions do not absorb the energy of all
light wavelengths equally. (The wavelengths of the visible spectrum are measured in nanometers
[nm], spanning about 400 to 700 nm. One nm is 10-9 meters; see the preceding table on linear
measures.)
The chlorophylls, for example, absorb strongly in the blue wavelengths (ca. 450 nm) and red
wavelengths (ca. 650 nm), but not in the green wavelengths (ca. 525 nm). Thus, plotting
absorbance versus visible wavelengths for a solution of chlorophyll yields a line that appears as
two mountains with a deep valley between. This topography, or absorption spectrum, is
characteristic of chlorophyll and may be used as an aid in its identification.
Quantitation is possible because at a given wavelength, absorption by a fixed thickness of a
solution is proportional to the concentration of solute (Beer-Lambert Laws). Since small amounts
of solute can be better detected at the wavelength where absorption is greatest (the absorption
maximum), it is customary to use the absorption maximum for such quantitative determinations.
A spectrophotometer consists of a light source, a device for selecting a specific wavelength, a
“slit” through which a narrow beam of the desired wavelength passes, a solution holder (known
as a colorimeter test tube, or cuvette), a photosensitive cell which measures the energy of light
transmitted through the solution, and a recording device such as a galvanometer.
A spectrophotometer must be calibrated at each wavelength so that the solvent of the
solution absorbs no light energy (i.e., transmittance is 100%). A “blank” is used to calibrate the
machine. The blank is a cuvette that contains everything except the specific compound for
which the absorption is being determined. After “blanking”—i.e., setting the machine to read
infinite absorbance when the cuvette holder is empty and 0% absorbance using the “blank”—the
test solution is inserted, and the absorbance is noted. Percent transmittance is not proportional to
solute concentration, but absorbance is proportional to solute concentration.
The transmittance scale is linear and reads from left to right, whereas the absorbance scale is
logarithmic and reads from right to left. One may read transmittance or absorbance directly
(realizing the log nature of the latter).
Using the Hach Odyssey DR/2500, the absorption for a solution can be obtained in two
ways:
1.
in 'Single Wavelength' mode the user can manually key in each wavelength, zero
the machine using a blank, then measure absorbance of the colored solution.
2.
in 'Wavelength Scan' mode, the user can have the machine automatically zero the
blank over a range of wavelengths, and then measure absorbance of the solution over the same
range.
Both operating modes will be outlined below.
88
Manual determination of absorption using Single Wavelength mode
1. Turn on the spectrophotometer with the blue power button on the lower left side and
let it equilibrate for 5 minutes. Select the 'Single Wavelength' function by pressing the
appropriate button on the screen touch-pad.
2. To select a wavelength (e.g. 420 nm), select the lambda (λ, a symbol meaning
wavelength) button on the touchpad, enter 4-2-0, and press “OK.”
3. Check the main screen to determine whether the spectrophotometer is set to measure
absorbance (Abs) or percent transmittance (%T). The unit of measurement appears
onscreen above the wavelength value; it is possible to toggle between these two
measurement units by pressing the small button on the right, below the folder icon.
4. Slide the Odyssey cover back to reveal the test tube holder. Polish, then place the
blank test tube (water only) into the test tube holder. Align the test tube so that the
“F” emblem faces (an index mark) the exclamation mark on the test tube holder. Slide
the cover forward to close.
5. Touch “ZERO” on the screen to set the absorbance reading of the blank to zero.
Absorbance should now read 0.000 (the reading may fluctuate slightly).
6. Open the cover of the spectrophotometer and remove the blank.
7. Polish, then insert the tube containing the solution to be tested into the tube holder,
aligning the index mark, close the cover and read absorbance.
8. Remove the tube and close the holder.
9. It is important that the instrument is calibrated or “blanked" each time the wavelength
is changed.
Automated determination of absorption maximum using Wavelength Scan mode
1. From the main menu select “Wavelength Scan”, then press the “Options” button to
adjust wavelength range and absorbance scale.
2. Select “λ range” on the touchpad. For an example, you may choose to set the lower
range to 420 nm by pressing the left on-screen button and entering 420 on the
numberpad. Press “OK.” Set the upper wavelength to 620 by pressing the right onscreen button and following the same procedure. Your range screen should now
display 420 nm – 620 nm. Press “OK.”
3. Select “Scale and Units” on the touchpad. Adjust the settings so that the screen
Scale: ~ Auto. Press “OK.”
appears as follows: Units: ~Abs
4. Press “Return” to get to the Wavelength Scan screen.
5. Insert the blank (water only) into the test tube holder and zero the machine (this will
take approximately 60 seconds).
6. Remove the blank tube and insert the tube containing the solution to be tested. Press
“Read” to begin the wavelength scan.
7. When the scan is complete, the machine will beep and display a graph of the results.
To determine the wavelength maximum, use the arrow keys on the touchpad to move
the vertical dashed line right or left, toward the highest point on the curve. Watch
the highlighted absorbance reading (above the magnifying glass icon) as you scroll
along the highest part of the curve and record the maximum absorbance reading –
this may occur along at more than one wavelength.
89
CHAPTER 13: OSMOMETRY
Principles of Function:
Osmolality (osmoles per kg solution) and osmolarity (osmoles per liter water) are
measures of the total concentration of dissolved particles in a solution, without regard for the
homogeneity or non-homogeneity of the molecular species, or their molecular weights, size,
or density. Any substance dissolved in a solvent affects four interrelated colligative
properties of the solvent-solute mixture. These colligative properties are 1) decrease in vapor
pressure, 2) decrease in freezing point, 3) increase in boiling point, and 4) a change in
osmotic concentration. All of these properties are interrelated and are mathematically
convertible. Therefore, the accurate determination of any one of these properties allows
estimation of the other three, and is a measure of the osmotic concentration of the solution.
Freezing point depression is one of the oldest and easiest methods for determination of
the osmotic concentration of biological fluids. By this method, the specimen is placed in a
cooling chamber which is maintained at a temperature well below the freezing point of the
solution. During the analysis, samples are supercooled, at which time crystallization is
initiated in a process called “seeding”. Seeding can be achieved by a variety of methods, and
in this case is performed by mechanical vibration. The crystal formation results in release of
the heat of fusion of water (80 cal/g water) causing the sample to warm to a point at which
ice and solution exist in equilibrium, and the temperature remains constant for a period of
time.
Osmotic concentration, or osmolality, is expressed in units of milliOsmoles (mOsm) per
kg of water, where one mOsm is equivalent to one mM of dissolved solute particles. A
solution containing 1 Osmole (1000 mOsm) of dissolved solute per kg of water lowers the
freezing point of water by 1.858°C. Therefore, the freezing point depression of the sample
can be converted to units of osmolality, or osmotic concentration by dividing by 1.858.
However, the osmometer does this calculation directly by converting the thermistor readings
by direct comparison with readings obtained by using standard aqueous salt solutions of
known osmolality.
Measuring with an osmometer:
1. When you get to the osmometer, the machine should be on, the Operating Head should
be down and there should be a clean, dry sample tube in the Refrigerator Well. Be sure
that when you are done using the equipment, it is in the same condition in which you
found it.
2. Be sure the osmometer is cooled: the cool light should be cycling on and off.
3. Raise the operating head by pushing the Head Release button.
4. The Range Switch should be in the 0-2 position and the Mode Switch in the RUN
position.
5. Place 50 uL of sample into an osmometer tube using the pipet provided. Avoid
capturing air in the bottom of the sample tube as this will adversely affect the
reproducibility of your readings.
6. Place the sample tube into the Refrigerator Well.
90
7. Lower the Operating Head so that the Seed Wire and Probe enter the tube, and the
Head latches in the down position, without forcing it.
8. The digital display will be reading negative numbers, which will start to become more
positive, and will count up to almost 1000. When the number hits 1000, the sample is
seeded with a high amplitude vibration of the seed wire (and the osmometer makes a
loud, obnoxious noise).
9. After the sample is seeded, the numbers on the digital display will decrease until they
read the osmolality of the solution. The READ light will come on and the head will
pop up when the reading is complete. The number on the digital display is the
osmolality of the solution.
10. Gently wipe off the Probe and Seed Wire with a clean kimwipe (DO NOT USE
GAUZE PADS).
Whenever the instrument is not in use, there should be an empty sample tube in the
Refrigerator Well, to prevent frost buildup in the Well. When you are finished using the
osmometer, you should turn it off and then lower the Operating Head into the empty tube to
protect the Temperature Probe. Do not force a sample tube into a frosted Refrigerator Well.
If the Well has accumulated frost, turn off the osmometer for several minutes to defrost, and
remove the accumulated moisture from the Well with a “Q-tip”.
91
CHAPTER 14: USING MICROSCOPES
1. Treat a microscope gently. Each one costs about $1200.
2. Always use both hands when carrying a microscope. Place one hand under the base
(bottom) of the microscope after lifting the microscope by its arm.
3. Do not move the lens down toward the slide to focus it; instead, run it down as far as it
will go without touching the slide while watching from the side. Then move the lens up
(turning the knob toward you) until it is in focus.
4. There are times when rule 3 does not strictly apply; the scanning lens cannot possibly go
far enough down to touch the slide. In addition, you may want to make the fine
adjustment with the low or high power lens in a downward direction. The important point
is that you make a habit of never focusing down without being absolutely sure that it is
safe.
5. Microscopes are good only if they are clean. Use only microscope lens cleaning paper to
clean lenses, a single slight scratch can ruin the lens. NEVER wipe the lens using a
circular motion; instead, tear a small piece of lens paper and sweep ONE TIME across
the lens. Use a different piece of lens paper for each subsequent sweep, if necessary.
6. Whenever possible, hold the slide up to the light to identify the location of the specimen
on it before placing it on the microscope stage.
7. You will save yourself a lot of time by always focusing under scanning or low power,
then moving the higher power lenses into place. The microscopes are parfocal; that is,
once the specimen is in brought into focus under one objective, it should remain in focus
under all other objectives. To change from one objective to another, turn the nosepiece;
do not push or pull on the individual objectives.
8. Keep the iris diaphragm closed as far as possible. Increasing the amount of light on the
specimen decreases the depth of focus and the contrast.
9. To minimize eye fatigue, train yourself to keep both eyes open when looking through the
ocular.
10. When observing living microorganisms, put a TINY drop of water containing the
organism on the slide, followed by a tiny drop of slowing agent (“Protoslo”), if desired.
To minimize air bubbles, hold the coverslip at an angle over the drop, with one edge
touching the slide. Then lower the opposite edge of the coverslip to cover the drop. With
larger microorganisms, like Paramecium, make a thin, uniform bead of Vaseline all the
way around the edge of a plastic coverslip. Drop the coverslip on the slide, and slightly
tap the top of it to make a good seal.
11. When drawing magnified specimens, always record the magnification at which the
drawing was made. (The magnification of an object under the microscope is the product
of the magnifications of the two lens systems, the objective and the ocular.)
92
12. When changing slides and when putting the microscope away, always position the
scanning lens down.
13.
If you have trouble seeing what you are looking for, ask the instructor for help.
Ocular (10X)
Nose
Piece
Objective
lenses
Arm
Stage
Iris Diaphragm
Coarse Focus
Fine Focus
Light
Source
Power Switch/ Light
Adjustment Knob
Figure 14-1. A compound microscope.
93
CHAPTER 15: USING FIRST SEARCH AND OTHER LIBRARY
RESOURCES
This library information is provided to familiarize you with various resources available in the library
and online that you will use when writing your Biology lab proposals and reports.
Finding and citing literature in formal lab reports
When preparing your formal lab reports, you will be expected to do the following:
1) Find at least four sources (journal or magazine articles, books, online articles) that are relevant
to your Biology lab topic.
Two of the sources must be from the primary literature (i.e. scholarly, peer-reviewed
journals). These include:
Original research articles: report original research
Review articles: give an evaluation of primary literature
Up to two of the sources may be from a book or edited book chapter
Book by single or multiple authors: all information in the book is written by the
author(s) on the book cover. Your textbook or lab manual can count as one of these
sources, the other three must be non-131 course material sources.
Edited book: contains a series of articles, each usually contributed by a different
author. Each chapter is considered a different source citation. See the Student
Survival Guide on how to cite books vs. chapters from edited books.
2) In your lab reports, show where you use information contained in these sources by
a)
correctly citing each in the text of the report. See "Citations within Text" in Chapter
6 of the Survival Guide.
b)
providing a complete, correctly formatted citation for each source in the Literature
Cited section of your proposal or report. See "Literature Cited Page" in Chapter 6 of the
Survival Guide.
3) attach a photocopy or printout of the first page of each cited source to the end of the report (do
this for the first lab report only). A photocopy from your textbook or lab manual is not necessary.
Remember, you must cite at least three sources in addition to your text or lab manual, and at least
two of these must be primary sources (journal research or review articles).
Finding and citing literature in lab proposals
When preparing lab proposals, you will go through the research process outlined below to find at
least two sources relevant to your lab topic. At least one must be an outside source. Provide
appropriate citations in the text of your proposal, and full citations in the Literature Cited section.
You do not need to include photocopies of your sources.
Explore Your Topic
• Your research will be easier if you understand what you are looking for.
• If possible, write down your topic in the form of a question.
• Write down the words that describe the main concepts of your topic.
• For each concept within your topic, list as many synonyms and related words as possible.
• For help finding these synonyms and understanding your topic, use sources that will give you
background information, including:
Biology textbooks
Specialized dictionaries and encyclopedias, such as:
94
Dictionary of Microbiology and Molecular Biology
McGraw-Hill Dictionary of the Life Sciences
McGraw-Hill Encyclopedia of Science and Technology
Search for Information - Books
•
•
Use GABRIEL, the Library Catalog: http://gabriel.sewanee.edu
Do a TITLE search for the books listed above.
ƒ
They will be found in the Reference area of the Library.
ƒ
Near these books will be others relating to Biology that will be useful.
ƒ
Often these books will lead you to other books, look for their references.
• Do a WORD search for books dealing with your topic using some of the words you wrote down
on your “Explore Your Topic” worksheet.
ƒ
Some of these should be in the General Collection of the Library.
ƒ
Near these books will be other related Biology books.
Search for Information - Articles
• Use a Biology Index.
ƒ
The most useful indexes are listed on the Biology Research Guide on the Library web page:
http://library.sewanee.edu
ƒ
Two important indexes are: BasicBIOSIS, BioAgIndex (Biological and Agricutural Index).
• Do a Keyword search using at least two of the concepts that you wrote down on your “Explore
Your Topic” worksheet.
ƒ
If you get too many results, add a concept to narrow down your search.
ƒ
If you get too few results, take out a concept or change the wording to expand your search.
• Choose several useful articles from your results.
Locate the Journal and Magazine Articles in the Library
• Use GABRIEL, the Library Catalog.
• Do a TITLE search for the title of the journal (not the title of the article).
• Also, check the list of Electronic Journals from the Library web page.
If the Library owns the journal:
ƒ
Note the years we own to make sure we include the year of your article.
ƒ
Note the call number and go to the shelves to find the journal.
If the Library does not own the journal:
ƒ
Order your journal article via Interlibrary Loan (forms available at the Reference Desk or
from the Library web page) -- this process can take some time, two weeks or more.
ƒ
Choose another article to locate in the Library.
Get Help Along the Way
• Reference Librarians are in the Library to help you with your research.
• Call them at 598-1368
• Email them askref@sewanee.edu
Other Helpful Sources
•
•
A Short Guide to Writing About Biology
Scientific Style and Format: the CBE Manual for Authors, Editors, and Publishers