AP Biology
How to DO Science……
AP Biology Lab Expectations
• Expected that AP students have acquired basic
lab and math skills
• New curriculum requires that students receive
little guidance when conducting lab work
• Students are expected to perform and report
experiments just like a research scientist
• Will follow the basics of the scientific method
Basic Scientific Method
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Background information
Hypothesis
Experimental procedure/design
Results
Conclusion
Universally accepted characteristics of
an investigation/experiment
• You must know something about the topic before
developing the investigation
• Classroom and research knowledge – helps you come
up with questions
• Hypothesis – clear, in correct format
• Null Hypothesis: the hypothesis of no difference or no
effect
• Your experiment will be trying to disprove your HO
• Alternative Hypothesis: hypothesis that experiment will
test
• Trying to prove your HA
Universally accepted characteristics of
an investigation/experiment
• Independent variable : the “cause”, what you are
testing the effect of, what you are doing
differently to your groups
• Dependent variable : the effect, what you are
measuring, the response to what you are doing to
each group
• Controlled variables: other things that could
affect your results if they aren’t controlled/the
same for all groups
• Experimental group(s)
• Control group
Preparation for our first inquiry lab
• Learn how to evaluate research articles – sections will
be the same in your lab write ups
• Familiarize with online sources of primary and
secondary sources – to use in your own work
• Practice creating and interpreting graphs
• Use Excel to properly graph data
• Learn how to use proper statistical calculations
• Use Excel to perform statistical analysis of data
• Practice lab write up and mini poster
• Practice those lab skills we will use all year
• Perform our first official AP Biology inquiry lab
You must become proficient at reading
and understanding scientific papers
• You will be writing your own!
• Primary vs. Secondary sources in science
• Primary Research Articles: document a scientist uses to
communicate results to other scientists
- submitted to peer reviewed journal
- original report, includes detailed
methods and results
• Secondary Sources: usually a summary of a research
project, written by scientist or journalist
• We will concentrate on primary sources
• Can you tell the difference?
Sources of Primary Research Articles
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Some will be provided in class
The journal Science – the father of all journals
Mostly you will be looking for sources online
Need to refine your searching skills
www.biomedcentral.com
www.ncbi.nlm.nih.gov/pubmed
www.highwire.org
Can go to journal websites directly and search –
usually just abstracts
Reading primary sources can be tough
• Very technical
• Experiment is testing a hypothesis, trying to
show results support hypothesis
• Trying to analyze if research is valid
• Will be looking up many words that you don’t
know in order to understand what you are
reading (real scientists do this….are you
above them?)
Anatomy of a primary source
Reference sample provided
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Citation
Abstract – summary: purpose, design, results, conclusions
Introduction – aka literature review, will include hypothesis
Materials and Methods: independent, dependent and
controlled variables, control groups, experimental groups,
repeatability
• Results: present and analyze data, charts, graphs, statistical
calculations and tests, statistical significance of results – no
describing or reasons for data – JUST THE FACTS/DATA
• Discussion: explain what the results and/or findings mean,
support or refute hypothesis, sources of error or potential
criticism, connections to other projects, implications, next
steps
The Basics of Graphing
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Most covered in provided packet
Independent variable on X-axis
Dependent variable on y-axis
MUST LABEL AXES – INCLUDE UNITS
Graph title
Legend
Short explanation so its super duper clear
what the graph represents
• Bar graphs: These are best used to show
numeric data that represent discrete items or
experiments.
• Line graphs: These are used to best represent
data that are samples from continuous
phenomena.
• Scatter plot: These graphs show the relationship
between two measured variables as a scatter of
individual points, each representing an item with its
position determined along the X and Y axes by its
values for the two variables. The points of a scatter
plot are never connected, but a regression line (a “best
fit” line) is often plotted, showing how one
measurement varies in relation to the other.
Little reason to graph by hand….
• Microsoft Excel is universally accepted when it
comes to data manipulation and graphing
• Only trick is to enter your data in a way that
the automatic graphing function recognizes
• You should have some sense of what the
graph should look like so you can recognize
problems……
• Computers aren’t always right
• Practice making graphs using Excel
• Hubbard Brook data manipulation activity
Linear Regression
• Analyze association between two variables –
changes in one variable cause changes in
other variable
• Fits a straight line to the data and gives values
of slope and intercept
• Y=mx+c gives you a formula from which you
can predict values
• Use trendline feature of Excel
Statistics
• a branch of mathematics that provides techniques to
analyze whether or not your data is significant
(meaningful)
• Statistical applications are based on probability
statements
• “I am 95% confident that I have proven my hypothesis”
• Nothing is 100% “proved” with statistics
• Statistics are reported
• Statistics report the probability that similar results
would occur if you repeated the experiment
First Hurdle
• Decide which statistical test to use
• Need to decide this when designing your
experiment
• So you collect correct amount of data and can
perform a valid statistical analysis
Statistics deals with numbers
• Need to know nature of numbers collected
– Continuous variables: type of numbers associated
with measuring or weighing; any value in a continuous
interval of measurement.
• Examples:
– Weight of students, height of plants, time to flowering
WHAT KIND OF GRAPH WOULD YOU USE FOR THESE?
– Discrete variables: type of numbers that are counted
or categorical
• Examples:
– Numbers of boys, girls, insects, plants
WHAT KIND OF GRAPH WOULD YOU USE FOR THESE?
Can you figure out…
• Which type of numbers (discrete or continuous?)
– Numbers of persons preferring Brand X in 5 different
towns
– The weights of high school seniors
– The lengths of oak leaves
– The number of seeds germinating
– 35 tall and 12 dwarf pea plants
– Answers: all are discrete except the 2nd and 3rd
examples are continuous.
Populations and Samples
• Population includes all members of a group
– Example: all 9th grade students in America
– Number of 9th grade students at EHS
• Sample
– Used to make inferences about large populations
– Samples are a selection of the population
– Example: 6th period Biology
• Why the need for statistics?
– Statistics are used to describe sample populations as estimators of the
corresponding population
– Many times, finding complete information about a population is costly
and time consuming. We can use samples to represent a population.
– NEED TO MAKE SURE YOUR SAMPLE IS NOT BIASED – RANDOMLY
SELECTED, etc.
Normal Distribution
• Most of the time we will assume that the measurements we
take of a sample population fall into a normal distribution
• Typical of natural phenomena
• Frequency of data points in a population
Distribution Chart of Heights of 100 Control Plants
Class (height of plants-cm)
Number of plants in each
class
0.0-0.9
3
1.0-1.9
10
2.0-2.9
21
3.0-3.9
30
4.0-4.9
20
5.0-5.9
14
6.0-6.9
2
Histogram
Number of Plants in each Class
35
30
25
20
Number of plants in each class
15
10
5
0
0.0-0.9 1.0-1.9 2.0-2.9 3.0-3.9 4.0-4.9 5.0-5.9 6.0-6.9
This is called a “normal” curve or a bell curve
This is an “idealized” curve and is theoretical based on an infinite number
derived from a sample
• Fifty percent of the distribution lies to the left
of the mean and fifty percent lies to the right
of the mean.
Descriptive Statistics
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Mean
Median
Mode
Standard Deviation
Standard Error
Mode and Median
• Mode: most frequently seen value (if no
numbers repeat then the mode = 0)
• Median: the middle number
– If you have an odd number of data then the
median is the value in the middle of the set
– If you have an even number of data then the
median is the average between the two middle
values in the set.
Mean (aka average)
Is the mean always an accurate
representation of your data?
• If collect this data:
5,5,4,5,5,6,5,5,5,5 mean = 5
• If you collect this data:
1,1,1,1,1,9,9,9,9,9 mean = 5
Which mean is really reflective of your
measurements? More reliable?
Usually need to add a calculation that determines
the spread of numbers around the mean…….
Measures of Variance…
• Range = largest value – smallest value
• Not really all that valuable…..
• Variance =
• Mathematically expresses the degree of variation of
scores from the mean
• Large variance = individual scores deviate a lot from
the mean
• Small variance = scores deviate very little from the
mean
• Standard deviation =
Remember our original means….
• 5,5,4,5,5,6,5,5,5,5 mean = 5
• Variance = 0.222
• Std Dev = .47 (this means each measurement varies +/.47 from the mean)
• 1,1,1,1,1,9,9,9,9,9 mean = 5
• Variance = 17.8
• Std Dev = 4.21 (this means each measurement varies
+/- 4.21 from the mean
Which mean is more reliable?
• The spread of a normal distribution is
controlled by the standard deviation, . The
smaller the standard deviation the more
concentrated the data.
So how does our sample mean
compare to the actual population
mean?
• Use standard error
• This relates the standard deviation to the size
of your sample
• Larger samples = lower standard error (more
like the overall population)
• Smaller samples = higher standard error (not
sure if really representative of actual
population
Practice….
• Mini experiments
• Using excel to make calculations/statistics
Comparative Statistics
• Compare two sets of data to see if they are
basically the same or if one set is significantly
different than the other
• Gives us a probability (P) that the null hypothesis
is true (that both groups are the same)
• Differences are significant if they have a P of less
than 5% (we reject the null hypothesis…aka
accept the Ha)
• If P is larger than 5% then there is no significant
difference between the two groups (we accept
the null hypothesis)
Chi Square
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Used with discrete values
Phenotypes, choice chambers, etc.
Not used with continuous variables
O= observed values
E= expected values
Gives you a value that you can then
compare to a critical value on a chart
For Example…
• If toss a coin 200 times, we would expect to see 100 heads and 100
tails
• These are our expected values
• When we actually toss the coin, we see 82 heads and 118 tails
• These are our observed values
• Ho = There is no significant difference between expected and
observed – difference is due to random chance
• Ha = There is a significant difference between expected and
observed
• Are the differences between observed and expected a significant
difference or a difference due to chance?
• Plug into Chi square formula
• 6.4
Interpreting a chi square
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Next calculate degrees of freedom
# of events, trials, phenotypes -1
Example 2 values-1 =1
Refer to Chi square chart
Generally use the column labeled 0.05 (which means
there is a 95% chance that any difference between what
you expected and what you observed is within accepted
random chance.
• Any value calculated that is larger means you reject your
null hypothesis and there is a difference between
observed and expect values.
How to use a chi square chart
Results
• According to the Chi chart, we can reject our
null hypothesis (accept Ha)
• The differences we see are not due to random
chance, they are significant differences,
something else is going on here to affect the
outcome.
So try it…..
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Toss a dice 120 times
1= 22
2 = 18
3 = 20
4 = 23
5 = 18
6 = 19
1.2
Student T-test
• Most common, used when there are two sets
of normally distributed data to compare
• Compare the sample means to each other
• Use equation to calculate the t-value
• once have t value, need
degrees of freedom
•Degrees of freedom = total
number of samples – 2
•Then refer to t value table,
critical value
• Ho = There is no significant difference
between the means
• Ha = There is a significant difference between
the means
Values in table are called critical
values – must meet or exceed
critical value in order to reject null
hypothesis
So if you calculated a t
value of 2.34 and had 11
degrees of freedom…..
Best to use Excel for this test….
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TTEST (range1, range2, tails, type)
Use 2 tails and type 2
Range 1 = cells that contain data set 1
Range 2 = cells that contain data set 2
Returns P value = probability that the two
means are the same (not significantly
different)
Pea Plant Normal Distribution Curve with Std Dev
The Normal Curve and Standard Deviation
A normal curve:
Each vertical line is
a unit of standard
deviation
68% of values fall
within +1 or -1 of
the mean
95% of values fall
within +2 & -2 units
Nearly all members
(>99%) fall within 3
std dev units
http://classes.kumc.edu/sah/resources/sensory_processing/images/bell_curve.gif