Table of Contents
TBLS lab report format and rubrics ............................................................................................................... 2
Biology lab report anchor paper ................................................................................................................... 7
Academic integrity ...................................................................................................................................... 15
Addressing measurement uncertainty ....................................................................................................... 16
Useful calculations ...................................................................................................................................... 18
Microsoft Excel quick reference ................................................................................................................. 19
Editing protocol for lab reports .................................................................................................................. 22
Data analysis editing checklist .................................................................................................................... 23
Evaluation editing checklist ........................................................................................................................ 24
Final draft editing checklist ......................................................................................................................... 25
TBLS lab report format and rubrics
Name
Course
Teacher Name
Due Date
TBLS labs contain these sections and each section heading is underlined and bold. The categories Design,
Analysis and Evaluation do not need to be included.
DESIGN
Research Question
One-sentence question
Background
Paragraph giving scientific context and background related to the
investigation and explaining personal interest in the topic
Hypothesis
Stated prediction with reasoning
Variables
Table with the following column headers: Variable name, Variable type,
and Method of control. Rows include IV, DV, and constants.
Materials
Bulleted list of equipment
Diagram
Labeled diagram of setup
Method
Numbered list of steps
Ethics
Bulleted list of safety, ethical, and environmental issues related to the
investigation
ANALYSIS
Raw Data
Raw data, may also contain processed data if appropriate
Uncertainties
Bulleted list of measurement uncertainties with justifications for each
Qualitative Data
Brief paragraph of observations
Sample Calculations
Formula and a worked example for each calculation
Processed Data
Table and graphs presenting calculated values
EVALUATION
Conclusion
Paragraphs discussing the results and comparing them to previous
research and accepted scientific knowledge
Evaluation
Paragraphs discussing sources of error and suggested improvements
2
Design and Exploration Rubric
Formulating a
focused
research
question
Rubric Average: _______ / 4.00
4
3
-the purpose of the investigation is
clear and narrowly focused on the
study of a plausible cause and effect
relationship
-unambiguously identifies relevant
independent and dependent
variables with appropriate
measurement words
-the purpose of the investigation is
plausible and focuses on a cause and
effect relationship, but the scope of
the investigation may not be fully
focused
- identifies relevant independent and
dependent variables with appropriate
measurement words
-fully and accurately explains all
scientific concepts relevant to the
Contextualizing the investigation and uses them to
investigation formulate a plausible hypothesis
Identifying
variables
Materials and
Diagram
Methodology
-identifies all relevant constant
variables unambiguously, using
accurate measurement words
whenever possible
100-scale: _______ = 70 + (Rubric Avg – 2.00) * 15
2
-identifies a topic for scientific study,
but the purpose of the investigation is
unclear and/or
- identifies independent and dependent
variables of some relevancy but they
may be poorly defined; measurement
words are lacking where they are
needed
1
-the topic of the investigation is
unclear or implausible or
-fails to identify an independent
or dependent variable, or
identifies variables that are
irrelevant to the proposed topic
-accurately explains most scientific -identifies some scientific concepts;
-identifies no relevant scientific
concepts relevant to the investigation; their relevance to the experiment may concepts; little effort is made to
may fail to ground hypothesis in these be trivial, or they may be superficially contextualize the investigation
concepts
or inaccurately explained
-identifies almost all of the relevant -simplistic understanding of constant -fails to control variables
constants with measurement words; variables; may omit many or include
necessary for making a
may omit or poorly phrase one or two variables that are outside of the control meaningful conclusion
of the experimenter (e.g. air pressure); a
number of variables may lack
measurement words
-selection of tools is fully
-selection of tools is generally
appropriate to the investigation and appropriate and most are clearly
all equipment is clearly identified by identified, with some gaps or
its name, size, and uncertainty
inconsistencies
-includes a clear, fully labeled
-includes a diagram that clarifies
diagram that clarifies the most
some steps of the method; it may be
important steps of the procedure
incompletely labeled
-selection of tools is consistently
inappropriate, or it is not possible to
determine appropriateness because
tools are insufficiently identified
-diagram may be missing, unclear, or
inconsistent with the procedure; it may
depict trivial steps that don’t require
further clarification
-method is specific enough to
replicate exactly; each procedure
step flows from the previous one
and includes the measurements
timing, and materials involved
- repetition steps are included when
needed
-method allows for the collection of
-method is vague or overly
data, but significant gaps or vagaries
general; steps for measuring the
make it difficult to replicate
independent and dependent
-there may be inconsistencies within the variables are unclear or omitted
method and table of variables that call
into question the quality of the data and
the control of constants
- method is usually specific; some
measurements, timing, and materials
may not be specified when they need
to be
- repetition steps may be missing or
unclearly phrased, so it is not clear
how to collect all raw data
-report shows little
understanding of the equipment
involved in the investigation;
scientific tools are rarely
mentioned, or tools are used
incorrectly
Considering safety, ethics, and the environment: The report shows full awareness of the safety, ethical, and
environmental issues involved in the investigation. If there is no mention, or if the report only acknowledges the need
for awareness without identifying specific issues relevant to the investigation, deduct 2 points from the design rubric
total before averaging.
3
Analysis Rubric
4
2
1
-report includes raw data that spans
an appropriate range of conditions,
and a sufficient number of trials are
planned, but data collection may be
incomplete
-some qualitative data but it may be
shallow or inappropriate
-some relevant data is collected but -report contains insufficient relevant
it may not span an appropriate range data; no valid conclusions can be
of conditions, may not include
reached that relate to the research
repeated trials, or may be missing question
qualitative data; as a result, the data
will support only a very simple
conclusion
-clear sample calculations, including
formulae and worked examples,
demonstrate that all processing has
Calculations
been done accurately
-calculations usually include
formulae and a worked example, and
the processing is generally accurate,
but there are minor errors or
omissions
-sample calculations are consistently - sample calculations are missing or
missing either a formula or a
too unclear to verify the accuracy of
worked example or
the work or
-processing contains significant
-processing is too inaccurate for a
inaccuracies
valid conclusion to be drawn
-chooses calculations that analyze
both average and variance
-presents the results in an appropriate
graph; if appropriate, the graph has a
Data
justifiable trend line
Processing and
-if needed, error bars correctly show
Graphing
either the variance or measurement
uncertainty, and the trend line passes
through all error bars
-chooses calculations that analyze
both average and variance
-presents the results in an appropriate
graph; there may be a trend line with
some basis but it attempts to over-fit
the data
- error bars are needed but they may
be missing, poorly chosen, or plotted
incorrectly
-some basic data processing is
carried out, but it is simplistic
-presents results in a graph that
shows a relationship between the IV
and DV, but the type of graph may
be inappropriate for the data set; it
may include a trend line with no
basis in the data, or a trend line is
missing but needed
-data are presented in tables that are
generally well organized and
formatted with legible headings
-graphs have an appropriate size and
scale
-there are some minor formatting
inconsistencies
-raw and processed data are
presented in tables, but they may be
poorly organized; some data may be
repeated or out of place
-graph may have a confusing or
inappropriate scale
-awkward formatting or page breaks
hinder interpretation
Raw Data
Quality
and
Quantity
Uncertainty
-report includes raw data that spans
an appropriate range of independent
variable conditions and has a
sufficient number of trials
-quantitative data is supported by
detailed and relevant qualitative
observations
3
-data processing neglects a critical
calculation or inappropriate
calculations limit ability to draw a
conclusion or
-graphing is too inappropriate or
limited for a valid conclusion to be
drawn from the data (i.e., there is no
graph at all, or the graph does not
allow analysis of the relationship
between IV and DV)
-accurately describes the uncertainties -identifies the uncertainties involved -shows a simplistic understanding of -no measurement uncertainties are
of all measurements, records data to a in measurements, but may lack
uncertainty; a few uncertainties are reported, or
place value that agrees, and maintains nuanced understanding; there may be missing where they are needed or -most or all uncertainties are
a consistent uncertainty throughout inconsistencies in the way data has included where they are
incorrect or inappropriate
-if necessary, propagates uncertainty been recorded
inappropriate; recording of raw or
fully and accurately
-propagates uncertainty when it is
processed data may consistently
necessary, but there are errors
ignore uncertainty
-data are presented in well organized
tables that are formatted with clear
borders, even spacing
-headings are easy to read; a line
Data
break separates variables from the
Presentation
unit and uncertainty
-graphs have an appropriate size and
scale
-raw or processed are not
consistently presented in tables, or
-tables are so poorly organized or
formatted that it is unclear what data
has been collected or how it was
processed
4
Evaluation Rubric
4
-fully and accurately summarizes
trends in the data (or the lack of
trends); if possible, correctly
determines a mathematical
relation between IV & DV
-uses data to construct a logical
Concluding based
argument that answers the RQ
on data
-evaluates the reliability and
scope of the conclusion based on
the quantity and quality of data
collected, and revises the
conclusion appropriately
-uses accepted scientific theory to
explain trends in data and, if
necessary, explains how data
differs from accepted theory
Contextualizing the
-if appropriate, determines or
conclusion
finds an accepted value and
accurately calculates the % error
of experimental values
Identifying
limitations
Evaluating
limitations
Suggesting
improvements
-identifies sufficient relevant
limitations resulting from the
experimental design and
methodology
3
2
1
-accurately summarizes trends in the -conclusion offers an answer to the
data, lacking specificity; may miss research question that could be
nuances or fail to determine a
supported by the data presented, but
mathematical relationship when it is references to the data are unclear or
possible
missing
-data is used to address the research -the argument may have many
question but the logic may have some logical leaps or inconsistencies
inconsistencies
-may make a general comment
-comments on the reliability or scope about the strength of the conclusion,
based on the available data, but may but fails to reference specific data
not revise the conclusion accordingly points or aspects of the conclusion
-conclusion does not answer the
research question or
-offers an answer that cannot be
substantiated by the data presented
or
-the conclusion is very simple and
little or no attempt has been made to
justify it with argument or data
-uses accepted scientific theory to
explain trends in data, but
explanation is incomplete
-may attempt to calculate % error,
but with some mistakes
-may refer to a relevant scientific
theory, but does not clearly explain
how it relates to the data collected
-does not reference an accepted
value when it is appropriate to do so
-makes no references to relevant
scientific theories when it would be
appropriate to do so, or inaccurately
describes science concepts related to
the conclusion
-identifies some relevant limitations
that result from the experimental
design, but one limitation may be
trivial; may overlook a notable
weakness
-identifies some relevant limitations, -does not identify limitations or
but they generally result from the
identifies only limitations that are
practical or procedural issues faced trivial or overly vague (e.g. “human
in the lab rather than design flaws error”)
-fully describes how all identified -describes how most identified
limitations skew the data higher or limitations skew the data or increase
lower or increase random error
random error, but shows incomplete
awareness of how one has impacted
data
-suggests that limitations have
-shows little awareness that
affected the quality of the data, but limitations may affect data
consistently fails to explain how the
data has changed as a result of these
limitations
-fully describes specific, realistic
procedural revisions and explains
how they would reduce the errors
identified previously
-very few realistic revisions are
offered; most suggestions are only
superficially described or do not
reduce errors that have been
identified
-describes some realistic procedural
revisions, but a few suggestions lack
specificity or do not clearly reduce
errors that have been identified
-offers no suggestions for
improvements or all suggestions are
superficial, irrelevant, or unrealistic
5
Communication Rubric
4
Scientific
Conventions
2
Report observes all of the
Report generally observes all of the Only three or four of the
following consistently:
conventions in [4], but there are a conventions in [4] are followed
-tables have titles that provide
few minor inconsistencies
with any consistency
relevant context
-column headers include variable
and units
-axis labels include variables and
units
-graphs include a title and, if
necessary, a legend
-graphs and images are labeled
“figure #:”, tables are labeled
“table #:”, and there is a consistent
numbering system
-tightly follows TBLS lab report
format and all major sections are
included and clearly labeled with
Formatting bolded headings
-page breaks are included whenever
and
Organization needed to prevent awkward
disruptions in tables or headings
-uses a consistent font and size
Clarity
3
-report is relevant and concise,
facilitating a thorough understanding
of the topic
-consistently and correctly uses
scientific terminology
-no or very few typos
1
One or none of the conventions in
[4] are followed consistently
- follows the general outline of the -vaguely follows TBLS lab report -the organization and division of the
TBLS lab report format and all
format, but section headings are work into sections is incoherent
sections are included, but some
missing or some sections are
headings or labels may be unclear missing
-there may be some awkward page -poor formatting may distract the
breaks or font usage but the outline reader from the content of the
of the report is clear
work
-report includes some irrelevant or
repetitive material that neither helps
nor hinders understanding
-consistently uses scientific
terminology, but may uses some
terms imprecisely
-there may be a few noticeable typos
-parts of the report contain irrelevant -much of the report is unclear as a
material that hinders understanding result of imprecise language or the
-often uses terminology inaccurately presence of irrelevant or
or relies on colloquial language
inappropriate material
-report may be riddled with typos,
but it remains comprehensible
6
Biology lab report anchor paper
Discipula Maxima
Magister Einstein
IB Biology HL
31 December 1999
Research Question
How does temperature affect the movement speed of Armadillidium vulgare?
Background
Innate behaviors have evolved to help organisms survive in their environment. An innate
behavior shown by many invertebrates is kinesis, an increase or decrease in random movement in
response to a stimulus. Typically, organisms increase their speed of movement when in unfavorable
conditions and slow down their movement in favorable conditions. This pattern allows them to spend a
greater time in favorable conditions, which is helpful for survival. Armadillidium vulgare, also known as
the pill bug, is a terrestrial invertebrate that is known to demonstrate kinesis in response to humidity. Pill
bugs are vulnerable to dehydration in dry climates. The kinesis behavior allows them to locate moist
environments and avoid water loss1.
The purpose of this experiment is to determine if pill bugs have a kinesis in response to
temperature changes in their environment. Pill bugs are ectotherms, so their internal temperature depends
on the surrounding environment. Temperature extremes are harmful, so it is plausible pill bugs will have
evolved an innate behavior to sense and respond to temperature changes.
Hypothesis
An increase in environmental temperature will decrease the speed of the pill bugs, because as ectotherms
they will prefer warmer temperatures to avoid freezing.
Table of Variables
Variable
Type
Method of Control
Temperature
(°C)
Independent
Three temperatures are tested: 15°C, 25°C, and 35°C. The pill bug
will be placed in a petri dish. The temperature will be changed by
placing the petri dish in either an ice bath, a warm water bath, or on
the table at room temperature. The temperature within the petri dish
will be measured using a standard thermometer (+/-0.5°C).
Movement
speed (cm/s)
Dependent
The floor of each petri dish will be lined with graph paper with 1cm x
1cm grid boxes. The pill bug’s motion will be recorded with the
camera of an iPhone for 2.5 minutes. When the video is played back,
the experimenter will count the number of grid boxes the pill bug
crosses during 30 second intervals. The speed of each interval will be
# 𝑜𝑓 𝑏𝑜𝑥𝑒𝑠
calculated: 𝑆𝑝𝑒𝑒𝑑 =
30𝑠
1
Campbell. Biology in Focus, edition 1e. New York, 2014.
7
Variable
Type
Method of Control
Dish size
Constant
All petri dishes are 10 cm in diameter and 0.5 cm tall.
Dimensions of
grid square
Constant
The squares of the graph paper are 1cm by 1cm for each trial and
condition, allowing an estimate of the distance traveled.
Equilibration
time
Constant
Before starting data recording, the pill bug will be placed into the
environment and allowed 1 minute of rest time to equilibrate to the
environment and overcome the shock of being moved by the
experimenter.
Trial time
Constant
Data will be recorded over 2.5 minutes (150s), in 30s intervals.
Rest time
between trials
Constant
Each pill bug is tested at all three temperatures, with at least a 3minute rest between trials in a petri dish at room temperature.
Light level
Constant
All trials will be conducted in the TBLS lab at a lab bench far from
the window, with the overhead lights off.
Camera
distance and
position
Constant
The iPhone camera lens is positioned directly over the center of the
petri dish, held in place by a ring stand at a height of 15 cm above the
petri dish.
Materials
 Pill bugs (5)
 10-cm petri dishes (2)
 Paper with 1cm2 gridlines, cut in a 10-cm diameter circle
 Ruler (+/- 0.05 cm)
 iPhone with video camera
 Ring stand and ring clamp
 Hot Plate
 1000-mL beaker
 Crushed ice or fresh snow
 Weigh boat (1)
 Thermometer (+/- 0.5°C)
 Wooden coffee stirrer
8
Diagram
Figure 1: Setup of petri dish for all conditions (ringstand not shown)
Figure 2: Setup for 15°C condition:
weigh boat with ice
Figure 3: Setup for 25°C condition:
9
Method
Preparation of the petri dishes (see diagram 1)
1. Data recording chamber: Trace the petri dish on the graph paper and cut out the traced circle with
scissors. Place the graph paper in one petri dish.
2. Rest chamber: Trace the petri dish on a paper towel and cut out the traced circle. Place the paper
towel in the chamber and moisten with tap water dropped from a pipette, to create a humid
environment that is safe for the pill bugs. Add just enough water to moisten the paper towel. Too
much water can drown the pill bugs.
3. Collect five pill bugs from the class tank and transfer them, one at a time, to the rest chamber.
Place the lid on the chamber.
For room temperature
4. Place the experimental chamber on the lab bench. Place a thermometer at the base of the
chamber. It should be at room temperature (20-25°C). Record the actual temperature.
5. Set up a ring stand with a ring clamp 15 cm above the center of the dish. Place the iPhone on top
of the ring clamp with the camera lens facing down.
6. Transfer one pill bug to the petri dish carefully using a wooden coffee stirrer.
7. Wait one minute for the pill bug to equilibrate to the new environment.
8. Start the iPhone recording. Observe the behavior of the pill bugs.
9. Stop the recording after 2.5 minutes. Transfer the pill bug to the rest chamber.
10. Repeat steps 6-9 using a new pill bug each time until all five pill bugs have been tested.
For ice bath (see diagram 2)
11. Fill a large weigh boat with crushed ice or fresh snow.
12. Place the experimental petri dish on the ice-filled weigh boat. Nestle it into the ice.
13. Position and adjust the ring stand so the camera is 15 cm above the floor of the dish.
14. Place the thermometer on the floor of the petri dish and monitor the temperature.
15. Once the temperature is 15°C, transfer the pill bug to the petri dish carefully.
16. Repeat steps 7-10 until all 5 pill bugs have been tested in this temperature.
17. Between each trial, place the thermometer on the floor of the petri dish. If the temperature is too
low, remove the petri dish from the ice temporarily until temperature is 15°C again, then resume
data collection.
For hot water bath (see diagram 3)
18. Fill the 1000-mL beaker with 600 mL of water and place it on a hot plate.
19. Place the prepared petri dish into the beaker and make it float on the water.
20. Insert a thermometer into the water.
21. Adjust the ring stand so that the camera is positioned 15 cm above the petri dish.
22. When the temperature reaches 35°C, place 1 pill bug in the petri dish.
23. Repeat steps 7-10 until all 5 pill bugs have been tested in this temperature.
24. Monitor the temperature throughout trials. If the water bath temperature gets too high, stop taking
data, turn off the hot plate and add ice to cool until it has reached 35°C again.
Analyzing data
25. View each video at low playback speed and count the number of boxes crossed by the pill bug
during each 30 second interval.
10
Safety and Ethics
Limit stress to pill bugs. Wash and rinse hands thoroughly before handling pill bugs, and always handle
them gently, one at a time. When pill bugs are not in use, they should be in the rest chamber with a moist
paper towel. If pill bugs show distress, stop the trial immediately.
Raw Data
Table 1: The effect of temperature on distance traveled by the pill bugs as recorded in 30-second intervals
Distance traveled per 30s interval
cm ± 0.5 cm
Temperature of the environment
°C ± 1 °C
15
22
35
0-30s
10.0
6.0
8.0
10.0
8.0
15.0
14.0
13.0
16.0
15.0
20.0
15.0
20.0
18.0
18.0
30-60s
12.0
8.0
6.0
10.0
5.0
13.0
16.0
11.0
13.0
13.0
17.0
18.0
18.0
20.0
20.0
60-90s
8.0
9.0
7.0
8.0
8.0
10.0
16.0
14.0
15.0
15.0
16.0
16.0
18.0
19.0
19.0
90-120s
8.0
5.0
10.0
6.0
7.0
14.0
12.0
12.0
14.0
14.0
18.0
19.0
19.0
17.0
20.0
120-150s
6.0
8.0
6.0
6.0
8.0
13.0
14.0
15.0
11.0
12.0
18.0
18.0
20.0
15.0
17.0
Uncertainties

Temperature (+/- 1°C): An analog thermometer was used with increments of 1°C. The measurement
uncertainty is +/-0.5°C. However, I continued recording data as long as the temperature was within
1°C of the desired temperature, so I chose to use a higher uncertainty value.

Distance (+/- 0.5 cm): Each grid box is 1cm in length. This works like an analog instrument, so the
measurement uncertainty is half the smallest increment.

Time (+/- 0.01s): The digital clock on the iPhone measures to the hundredth of a second, and I can be
this precise because I slowed down the video playback to get an accurate count.
11
Qualitative Data
When transferred into the container, the pill bugs rolled into a ball for a short time. When they unrolled,
they moved mostly in a circular fashion around the perimeter of the dish. They will often stop and turn
their fronts toward the petri dish wall, probing it with their antennae. Their movement patterns,
independent of speed, were similar at all temperatures.
Sample Calculations:
First I calculated the average speed per 30-second interval:
𝑆𝑝𝑒𝑒𝑑 =
𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒
𝑡𝑖𝑚𝑒
𝑆𝑝𝑒𝑒𝑑 𝑓𝑜𝑟 15°𝐶 𝑇𝑟𝑖𝑎𝑙 1 =
10 𝑐𝑚
= 0.33 𝑐𝑚/𝑠
30𝑠
Then I calculated the average speed over the 2.5 minute trial for each pill bug:
𝑠𝑢𝑚 𝑜𝑓 𝑎𝑙𝑙 𝑣𝑎𝑙𝑢𝑒𝑠
# 𝑜𝑓 𝑣𝑎𝑙𝑢𝑒𝑠
0.33 + 0.40 + 0.27 + 0.27 + 0.20 (𝑐𝑚/𝑠)
𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑓𝑜𝑟 15°𝐶 𝑇𝑟𝑖𝑎𝑙 1 =
= 0.29 𝑐𝑚/𝑠
5
𝐴𝑣𝑒𝑟𝑎𝑔𝑒 =
I averaged the average trial speeds for all of the pill bugs at the same temperature:
𝑠𝑢𝑚 𝑜𝑓 𝑎𝑙𝑙 𝑣𝑎𝑙𝑢𝑒𝑠
# 𝑜𝑓 𝑣𝑎𝑙𝑢𝑒𝑠
0.29 + 0.24 + 0.25 + 0.27 + 0.24 (𝑐𝑚/𝑠)
𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑓𝑜𝑟 15°𝐶 =
= 0.26 𝑐𝑚/𝑠
5
𝐴𝑣𝑒𝑟𝑎𝑔𝑒 =
Finally, to analyze variance amongst the pill bugs, I found the standard deviation of the average trial
speeds at the same temperature using the =STDEV.S function in Microsoft Excel:
=STDEV.S (0.29, 0.24, 0.25, 0.27, 0.24)
= 0.02 cm/s
I then plotted a scatterplot graph to show the relationship between the temperature of the chamber and the
average speed of the pill bugs. The trend appeared approximately linear so I plotted a linear line of best
fit.
12
Processed Data
Table 2: Calculating the average speed of pill bugs at different temperatures
Temperature of
the environment
°C ± 1°C
15
22
35
Speed of the pill bugs
at 30 intervals
cm/s
030s
0.33
0.20
0.27
0.33
0.27
0.50
0.47
0.43
0.53
0.50
0.67
0.50
0.67
0.60
0.60
3060s
0.40
0.27
0.20
0.33
0.17
0.43
0.53
0.37
0.43
0.43
0.57
0.60
0.60
0.67
0.67
6090s
0.27
0.30
0.23
0.27
0.27
0.33
0.53
0.47
0.50
0.50
0.53
0.53
0.60
0.63
0.63
90120s
0.27
0.17
0.33
0.20
0.23
0.47
0.40
0.40
0.47
0.47
0.60
0.63
0.63
0.57
0.67
120150s
0.20
0.27
0.20
0.20
0.27
0.43
0.47
0.50
0.37
0.40
0.60
0.60
0.67
0.50
0.57
Average
speed per
pill bug
cm/s
0.29
0.24
0.25
0.27
0.24
0.43
0.48
0.43
0.46
0.46
0.59
0.57
0.63
0.59
0.63
Average speed
of pill bugs in
each
temperature
cm/s
Standard
Deviation
0.26
0.02
0.45
0.02
0.60
0.03
Figure 4: Temperature Increases the Speed of Pillbugs.
Error bars represent +/- 1 standard deviation.
0.70
Average Speed (cm/s)
0.60
0.50
0.40
0.30
0.20
0.10
0.00
10
15
20
25
Temperature (°C +/- 1°C )
30
35
13
Conclusion:
The purpose of this investigation was to determine if Armadillidium vulgare shows kinesis in
response to temperature changes. The data suggests they do. There is a positive correlation between
temperature and the speed of pill bug movement, as shown by the trend line in the graph. Thus, pill bugs
move more quickly in warmer temperatures and more slowly in colder temperatures. This contradicts my
initial hypothesis. There was very little variation between pill bugs, as shown by the small standard
deviations. All pill bugs responded very similarly at each temperature, which supports the idea that this
behavior is genetically inherited.
The trend line shows a linear relationship, but one point (22°C) does not fit this pattern (the trend
line does not pass through the error bars of this point.) The change in speed was greater from 15°C to
22°C than from 22°C to 35°C, even though the temperature charge was not as large. This could mean that
the relationship is not linear, and that the pill bugs have a maximum speed at which the line plateaus. This
idea is speculative, as my experiment only tested a limited range of temperatures. My experiment also
does not allow for conclusions about the behavior of pill bugs in extreme cold.
The results of this experiment are consistent with other research that has shown that pill bugs
have a kinesis response to temperature. Pill bugs are vulnerable to desiccation, and higher temperatures
cause faster evaporation of water. Thus, it has been suggested that pill bugs increase their rate of
movement in warm conditions in order to boost the odds of reaching a more favorable environment2. Our
experiment supports this theory because pill bugs exhibited an increase in kinesis as temperature
increased, and the behavior was the same in all of the pill bugs.
Evaluation:
It was difficult to maintain the temperature of the petri dish. The cold chamber continued to cool
throughout the trials, leading to lower speeds. On the other hand, the hot water bath continued to heat up,
leading to higher speeds. It can still be said for sure that the cold trials and hot trials are relatively colder
and hotter than room temperature, but the exact temperatures are not known precisely. This could be
improved by conducting the trials in an incubator set at a stable temperature. The camera could be placed
in the incubator to record the trial. This would reduce the uncertainty of the temperature measurements.
Another limitation was that there were only three temperature conditions tested. As discussed
previously, this limits the conclusions that can be drawn. This could be improved by repeating the
investigation at other temperatures between 10°C and 35°C to better define the trend line. If there were
data points for 10, 20, 25, and 30°C, the trend line would be clearer and it would be possible to know if
the relationship was truly linear or not.
A final limitation was that the distance traveled was not measured directly. This distance was
approximated by counting the number of grid boxes crossed by the pill bug. Because the petri dishes are
circular, many boxes are incomplete at the edges. It takes less time to cross these, but it still counts as
crossing 1 full box, equal to 1 cm. As a result, this method overestimates the distance actually traveled.
The pill bugs spent most of the time moving circularly around the edges, so this error is probably
significant. This could be avoided in the future by using a square enclosure that just fits a 10 x 10 grid, so
that no boxes are cut off at the edges. Every box crossed would consistently be a 1 cm distance.
2
Cloudsley-Thompson. “Investigation of Behavior in the Terrestrial Isopod.” Science, 1952.
14
Academic integrity3
As in all disciplines, an authentic piece of work in science is one that is based on your individual
and original ideas. Where the ideas or work of another person are represented within your
work—whether in the form of direct quotation, paraphrase, or diagram—the sources must be
fully and appropriately acknowledged.
The integrity and honest representation of data is paramount in science—results should not be
fixed or manipulated or doctored. Data is the lifeblood of scientists and may be obtained from
pure observation or from specifically designed experiments. In all cases, data presented in a lab
write-up is intended to report what has taken place in the real world. If a student manufactures
or manipulates data for a table, survey or other such requirement, it is scientific and academic
fraud.
The following declaration must appear in your final work:
“I do hereby affirm that this work is my own original composition. I have written this report
myself in order to represent the ideas and thinking of my group. Except for the sources I have
acknowledged within, all ideas were developed by my group members and myself. Unless
otherwise stated, all data and observations were obtained in the TBLS lab using the procedures
described herein. I affirm that all data are authentic, and I have neither changed nor invented
data in the process of reporting and analyzing my findings.”
Citations
Citations commonly come up in the following sections of the lab report:





Background: To refer to previous research or scientific content related to your topic, you
must cite the research. Remember, your textbook also counts as a source!
Method: You must cite the original source of your measurement protocol! This applies
whether you used this lab manual or did your own web research.
Diagram: If you take an image from the Internet, you must cite the source.
Calculations: If you are calculating percent error by comparing your result to a standard
value, you must cite the source of the standard value.
Conclusion: When comparing your results to previous research and established
knowledge, you must cite the sources you used. If applicable, include your textbook.
You may cite either using footnotes or by making parenthetical references and including a
Works Cited as the final section of your lab. Either way, citations should be in MLA format.
Refer to the Online Writing Lab at Purdue:
https://owl.english.purdue.edu/owl/resource/747/01/
3
This text has been adapted from the International Baccalaureate Group 4 Subject Guides, first assessment 2016
15
Addressing measurement uncertainty
Random error in an experiment generally arises from the limited accuracy of the measuring
instruments. An uncertainty range applies to any experimental value. Some instruments, like a
digital scale, state the degree of uncertainty. Others, such as rulers or graduated cylinders, do
not. In those cases you have to make a judgment regarding the degree of uncertainty.
Examples of Instruments
Graduated cylinder,
thermometer, ruler, etc.
Digital scale, temperature
probe, etc.
Analogue
Digital
Fig. 1
Fig. 2
Rule for Uncertainty Values
The uncertainty value is ± half the
smallest division.
The uncertainty of a digital scale is
± the smallest scale division.
Fig. 3
Fig. 1 represents an alcohol thermometer with the smallest scale division of 1°C. The
uncertainty value is 0.5°C, so the temperature should be recorded as 23.5 °C ± 0.5°C. The last
digit in bold represents an estimated value, since this digit of the thermometer reading falls in
between the smallest scale divisions. This digit represents the reported uncertainty value.
Fig. 2 represents a volume reading taken from the bottom of the meniscus. The smallest scale
division is 2 cm3, so the volume should be recorded as 38 ± 1 cm3. The last digit in bold
represents an estimated value, since this digit falls in between the smallest increments. This
digit represents the reported uncertainty value.
Fig. 3 represents a digital scale. The mass is recorded as 329.43 ± 0.01 g. The digit of uncertainty
(in bold) is already implied within the reported value given by the instrument.
16
Place value in measurements
The digits in the measurement up to and including the first uncertain digit are the significant
figures of the measurement. These should correspond to the uncertainty values, and all data
should be recorded to the same decimal place.
Example:
CORRECT
✔
Trial 1
INCORRECT
✖
Initial Mass
g ± 0.01 g
101.43
Trial 1
99.89
102.56
101.00
100.00
Initial Mass
g ± 0.01 g
101.4
99.89
102.6
101.00
100.0
Other sources of uncertainty
Uncertainties in judgment, such as color change from an indicator, can be difficult to quantify.
These types of uncertainties should be noted even if they are not actually quantified.
Uncertainty in calculations
When performing a calculation, the answer should be rounded to the same number of decimal
places as the least precise measurement.
Examples:
1) A student records the mass of a solution of sodium chloride to be 5.00 ± 0.01 g, and the
volume as 2.3 ± 0.1 cm3. What is its density?
𝐷𝑒𝑛𝑠𝑖𝑡𝑦 =
𝑚𝑎𝑠𝑠
5.00 𝑔
=
= 2.173913043 𝑔 𝑐𝑚−3
𝑣𝑜𝑙𝑢𝑚𝑒
2.3 𝑐𝑚3
The uncertainty of the density calculation is determined by the volume measurement,
since it is less precise than the mass measurement. The second significant figure is
therefore the uncertain digit, and the reported value must be recorded as 2.2 g cm-3.
2) A student is asked to report the concentration of a solution prepared by adding 1.00 g of
sugar to 50 mL of water.
𝐶𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛 =
𝑚𝑎𝑠𝑠 𝑠𝑜𝑙𝑢𝑡𝑒
1.00 𝑔
=
= 51 𝑔 𝑚𝑙 −1
𝑣𝑜𝑙𝑢𝑚𝑒 𝑠𝑜𝑙𝑢𝑡𝑖𝑜𝑛
50 𝑚𝑙
The uncertainty of the concentration measurement is +/- 1 g ml-1. This value
corresponds to the least precise measurement, which is based on the uncertainty of the
graduated cylinder.
17
Useful calculations
Mean
Median
Half Range
𝑆𝑢𝑚 𝑜𝑓 𝑎𝑙𝑙 𝑣𝑎𝑙𝑢𝑒𝑠
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑣𝑎𝑙𝑢𝑒𝑠
The middle number when data are arranged
sequentially
𝑀𝑎𝑥 𝑣𝑎𝑙𝑢𝑒 − 𝑀𝑖𝑛 𝑣𝑎𝑙𝑢𝑒
2
Standard
Deviation
Easiest to do on a graphing calculator
or in Excel as =STDEV.S
Percent
Change
𝐹𝑖𝑛𝑎𝑙 − 𝐼𝑛𝑖𝑡𝑖𝑎𝑙
𝑥 100%
𝐼𝑛𝑖𝑡𝑖𝑎𝑙
Percent of
Initial Value
𝐹𝑖𝑛𝑎𝑙
𝑥 100%
𝐼𝑛𝑖𝑡𝑖𝑎𝑙
Percent of
Control
Used to find a single number that represents the
data set.
Same use as mean, but is best for data sets with a
significant outlier that would skew the mean.
Used to find a number that represents the spread
of the data. Half range can be used to plot error
bars.
Same use as half range, but is appropriate for
normally distributed data (like many biological
traits).
To show how much a variable has changed; useful
when the initial values are very different. 0%
indicates there has been no change.
Same use as percent change, but here 100%
means no change.
𝐸𝑥𝑝𝑒𝑟𝑖𝑚𝑒𝑛𝑡𝑎𝑙 𝑣𝑎𝑙𝑢𝑒 − 𝐶𝑜𝑛𝑡𝑟𝑜𝑙 𝑣𝑎𝑙𝑢𝑒
To compare experimental values to a control
𝑥 100%
group value.
𝐶𝑜𝑛𝑡𝑟𝑜𝑙 𝑣𝑎𝑙𝑢𝑒
Percent Error
𝐴𝑐𝑡𝑢𝑎𝑙 − 𝐸𝑥𝑝𝑒𝑐𝑡𝑒𝑑
𝑥 100%
𝐸𝑥𝑝𝑒𝑐𝑡𝑒𝑑
To compare an experimental result to a known
standard value.
Inverse Time
1
𝑇𝑖𝑚𝑒
To transform time measurements into rates, so
that the largest value indicates the fastest rate.
Rate
∆ 𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒
∆ 𝑇𝑖𝑚𝑒
Concentration
(molarity)
𝑀𝑜𝑙𝑒𝑠 𝑜𝑓 𝑆𝑜𝑙𝑢𝑡𝑒
𝑉𝑜𝑙𝑢𝑚𝑒 𝑜𝑓 𝑆𝑜𝑙𝑢𝑡𝑖𝑜𝑛 (𝐿)
Concentration
(% by volume)
𝑉𝑜𝑙𝑢𝑚𝑒 𝑜𝑓 𝑆𝑜𝑙𝑢𝑡𝑒
𝑥 100%
𝑉𝑜𝑙𝑢𝑚𝑒 𝑜𝑓 𝑆𝑜𝑙𝑢𝑡𝑖𝑜𝑛
Measure of concentration when the solute and
solvent are both liquid.
Concentration
(g / L)
𝑀𝑎𝑠𝑠 𝑜𝑓 𝑠𝑜𝑙𝑢𝑡𝑒 (𝑔)
𝑉𝑜𝑙𝑢𝑚𝑒 𝑜𝑓 𝑠𝑜𝑙𝑢𝑡𝑖𝑜𝑛 (𝐿)
Measure of concentration when the solute is a
powdered solid, and it is not practical to find the
molarity.
To compare how fast different reactions or
processes are happening.
Standard measure of concentration. Whenever
possible, this is the measure of concentration that
should be used.
18
Microsoft Excel quick reference
Basics of navigating Excel
You should notice that each cell has its own “address” comprised of its column letter and its
row number (ex: A1, G4, H10, etc.) These addresses come in handy when performing
calculations.
When using Microsoft Excel, it is very important to pay attention to where your cursor is and to
what you have selected. Your cursor can take on different shapes, and each shape has its own
function. A few common cursors in Excel:
White Cross
Cross with Arrows
Cursor for
selecting
cells
Double Headed Arrow
Drag: moves
entire
selected area
Black Cross
Drag:
Resizes
column or
row
Select the
corner of a
cell and drag
and it copies
whatever is
in that cell
Formatting your table
Resize a column or row – Place mouse over the line that separates the two columns or rows in
the reference row (capital letters for columns, numbers for rows). A special cursor will appear.
Click and drag the line to the left or right.
Insert column – Highlight the column to the right of where you would like to insert. Right-click
on the letter at the top then select insert.
Insert row – Highlight the column below where you would like to insert. Right-click on number
at the left edge and select insert.
Merge cells – Highlight the cells you wish to merge. Go to the Home tab and find the “Merge &
Center” button:
19
Create borders: There are many different options for creating borders. They can all be found in
the border drop down menu in the Home tab. Either highlight the cells you would like to place
borders around, or select “Draw Border” and draw them in yourself.
Formatting cells
Wrapping text inside a cell – Select the cell in which you would like the text to appear on
multiple lines and then press the “Wrap Text” button:
Creating a new line inside a cell – Hold down the Shift key while pressing Enter.
Changing the format that displays in the cell (decimal, scientific notation, date) – Select the
cell (or cells) whose format you would like to change. Right click on the cell and then select
“Format Cells” in the menu that appears. Under the Number tab, chose the format display you
would like to use from the Category list and then hit OK.
Changing the number of decimal places displayed – Follow the instructions above for changing
the format. When you get to the Category list, select Number, then adjust the number next to
decimal places. You can also do this more quickly by selecting the cells on your spreadsheet and
using the decimal place adjuster hot button in the editing menu. The left arrow decreases the
number of decimal places and the right number increases them, rounding appropriately along
the way.
20
Working with equations

Select the cell that you wish to place an equation in

Type an = and then the function or equation that you would like Excel to calculate
o * for multiply
o / for divide
o + and – for add and subtract
o Average(list of cells) to average

To reference a cell, simply click on that cell. The cell will appear by its column and row
designation, e.g. H7

To reference more than one cell, either click and drag if they are next to each other, or
hold down the CTRL key as you select the cells. The list of cells will appear separated by
commas or first and last cell in the list separated by a colon.
Example: =AVERAGE(E9,F9,G9,H9,I9) or =AVERAGE(E9:I9)
Creating and manipulating graphs

Select the cells that contain your independent variable data, then hold down the Ctrl
key and select the cells that contain your dependent variable data averages.

Once all cells are selected, go to the Insert tab, and click on the type of graph you want
to add (usually this will be a scatterplot with only markers).

Once your graph appears, use the tabs that appear under Chart Tools (Design, Layout
and Format), to manipulate your graph. You can add a title and axis labels, as well as
change the color and shape of your markers.
Adding a best-fit line – Under the Layout tab under Chart Tools, click on the Trendline dropdown menu, then select the type of best-fit line you would like to add. If none of the lines are a
good fit for your data, you may try to use the “Smooth Marked Scatter” option to add a smooth
line that connects all your points. If your data fluctuates up and down with no seeming pattern,
or if your error bars are large and overlapping, it may be a sign that there is no relationship in
your data. In that case, you should leave the trend line off.
Adding error bars – In a new column, enter the measure of variance for each of your data
points. Go to the Layout tab under Chart Tools and select the Error Bars drop down menu.
Select “More Error Bar Options.” Select the option at the bottom that says Custom, then click
the “Specify Value” button. Once the new window pops up, click on each of the two boxes and
select the column containing your uncertainties. Then hit “OK” and then close.
21
Editing protocol for lab reports
Instructions: Read through a partner’s draft with a pen in hand. Consider the following and
mark any issues you see. Write the feedback directly on the person’s draft.
1. Organization: Does the lab report contain all nine sections (Research Question, Background,
Hypothesis, Variables, Materials, Diagram, Method, Safety and Ethics, Works Cited)? Is the
start of each section easy to find and clearly indicated with bolded headings? Are there
awkward page breaks or formatting?
2. Citations: Does the lab report use parenthetical references to cite the use of ideas from
other sources and references that are presented in the lab? Look in the background
paragraph and method. The class packet should be cited.
3. Measurement words: Does the lab report consistently use measurement words (mass,
volume, length, etc – never “amount”) to name dependent, independent, and constant
variables? Are the units of measurement included whenever necessary? Is it clear how to
measure each variable in the lab?
4. Equipment: Whenever a verb like “measure”, “cut”, “transfer,” or “stir”, etc. appears, does
the lab report specify what tools to use? Is all equipment fully identified by its correct name,
size, and uncertainty? (Ex: 100-ml beaker or digital scale (+/- 0.1g))
5. Repetition: Is it clear which steps should be repeated, when they should be repeated, and
how many times, in order to collect data for all trials? Is it clear how many data points are
being collected in total?
6. Robot test: If a robot were following the instructions of the lab and could only do exactly
what is written, what steps would the robot get tripped up on? How could these steps be
broken down or described more clearly and specifically?
7. Consistency: Are the materials, procedure, table of variables, and diagram 100% consistent
with one another? For each constant, are there method steps that describe how to maintain
that constant? Does the method refer to all of the equipment in the materials list? Are
there any inconsistencies at all?
22
Data analysis editing checklist
Look closely at the raw data table:
 All of the columns have a unit in the column header.
Final Mass
 All of the columns with measurements have an uncertainty value listed.
g ± 0.1 g
 The unit and uncertainty are centered beneath the variable (see text box).
 All data in a given column is recorded to the same number of decimal places, and the number of
decimal places matches the uncertainty.
 Each column is about the same width and each row is about the same height.
 The table fits within the page margins.
 Data within the table is recorded as numbers only (no units).
 A title appears above the table that describes the data and the context of the experiment.
Look closely at uncertainties:
 Uncertainties are listed and justified using the uncertainty rules we learned.
 It is clear how each of the uncertainties listed relates to the raw data table. For example, for a
concentration variable, the uncertainty of a graduated cylinder and a scale should be listed.
Look closely at the qualitative data:
 The qualitative data is in the form of a descriptive paragraph.
 The qualitative data has NO commentary on the numbers (e.g. “the mass increased”).
 The qualitative data is useful and relevant to the experiment.
Look closely at the processed data table:
 Processed data table contains columns for the IV, average DV, and a measure of variance (half range
or standard deviation) for each average.
 All of the columns have a unit in the column header.
 The unit is centered beneath the variable (see above).
 Each column is about the same width and each row is about the same height.
 The table fits within the page margins.
 Data in the table is recorded as numbers only (no units).
 A title appears above the table that is different than the title for the raw data table.
Look closely at the sample calculations:
 Sample calculations are present for all calculated values in the processed data.
 Every sample calculation includes the formula.
 Every sample calculation includes one worked example.
 The worked examples consistently include the units of measurement.
 The calculations look neat and were completed using the Equation Editor in MS Word.
Look closely at the graph:
 Both axes are labeled with the correct variable and unit.
 The graph has a descriptive title that refers to both axes and identifies the trend or lack thereof.
 The graph takes up at least half a page.
 Vertical error bars are present, and there are no horizontal error bars.
 A sentence near the graph (or in the title) clearly states what the error bars represent.
 The error bars are plotted correctly and match the values in the processed data table.
 Unless there are many data points or the x-axis is time, the points are not connected by a line.
 There is a trend line (line of best fit) that seems to follow the pattern of the plotted points and
passes through all the error bars.
23
Evaluation editing checklist
Look closely at the conclusion:
 The conclusion completely describes the patterns revealed by the trend line in the graph. If there is
no trend, this is clearly stated in the conclusion.
 The conclusion is specific and includes minimum and maximum values, the range of tolerable
concentrations, etc. as appropriate.
 The conclusion explains what the patterns in the data reveal about the research question.
 The conclusion compares the findings of this investigation to what is already known about the topic,
identifying whether the investigation confirms, refutes, or expands current thinking.
 The conclusion cites sources as necessary.
Look closely at the evaluation:
 There are at least three paragraphs, each one addressing a different limitation.
 No more than one of the limitations is a practical or procedural error (such as a mistake that was
made or a lack of sufficient time).
Limitation 1
 The limitation is identified as a systematic or random error (if appropriate).
 There is a clear explanation of how the limitation affected the data that was collected. If the
limitation is a systematic error, the report clearly identifies whether the error skewed the data up or
down.
 There is a suggested improvement that would realistically improve the limitation in future work.
 The suggestion is specific, including equipment that would be needed and measurement values. The
suggestion reads like a procedure step.
Limitation 2
 The limitation is identified as a systematic or random error (if appropriate).
 There is a clear explanation of how the limitation affected the data that was collected. If the
limitation is a systematic error, the report clearly identifies whether the error skewed the data up or
down.
 There is a suggested improvement that would realistically improve the limitation in future work.
 The suggestion is specific, including equipment that would be needed and measurement values. The
suggestion reads like a procedure step.
Limitation 3
 The limitation is identified as a systematic or random error (if appropriate).
 There is a clear explanation of how the limitation affected the data that was collected. If the
limitation is a systematic error, the report clearly identifies whether the error skewed the data up or
down.
 There is a suggested improvement that would realistically improve the limitation in future work.
 The suggestion is specific, including equipment that would be needed and measurement values. The
suggestion reads like a procedure step.
24
Final draft editing checklist
Look at the entire document. Consider conventions, formatting, organization, and clarity.
 All of the required sections of the TBLS lab report format appear in the correct order, and each
section has the correct format.
 Report begins with a proper MLA heading.
 Report is NOT double-spaced.
 The report contains all sections with clear, bolded, and underlined section headings.
 There are no awkward page breaks (page breaks that interrupt tables, separate titles from the table
or graph they go with, or separate a section heading from the section it goes with).
 The report uses a consistent font style and size.
 There are no typos.
 Every table and graph has a title.
 Table titles are preceded by “Table #:” and a consistent numbering system is used.
 Titles for graphs and diagrams are preceded by “Figure #:” and a consistent numbering system is
used.
 Conclusion refers to data by table and figure # (e.g. “As seen in Figure 1…”) when appropriate.
 Redundant or contradictory information has been removed—report is concise and consistent.
25