Unit 1. Experimental Design
Name _______________________
Period________________
Activity 4. Catalase Activity Case Study
Key Idea: A simple experiment to test a hypothesis involves manipulating (changing the value or setting) of one
variable (the independent variable) and recording the response (the dependent variable or result).
Investigation:
Catalase is an enzyme that converts hydrogen peroxide (H2O2) to oxygen and water. An experiment investigated
the effect of temperature on the rate of the catalase reaction.
Procedure:
1. Test tubes (10 ml) were used in the experiment. Each tube contained 0.5 ml of dilute catalase enzyme
and 4 ml of H2O2. Reaction rates were measured at four temperatures (10oC, 20oC, 30oC, 60oC). For
each temperature, there were two reaction tubes (e.g. tubes 1 and 2 were both kept at 10oC). One
minute after the reaction, the height of the oxygen bubbles produced was measured and used as an
indicator of the reaction rate. More bubbles after one minute indicates a faster reaction rate.
2. The entire experiment was repeated two more times (each on a separate day).
QUESTIONS
1. What is the purpose of THIS experiment (no, the answer is not “the key idea” listed above). The purpose
should talk about the effect of the independent variable (the variable you are manipulating or changing)
on the dependent variable (what are you measuring).
2. Given your purpose, what is a possible hypothesis for this experiment? (How do you think the
independent variable will affect the dependent variable?
3. Name the independent variable in this experiment ___________________________
a. What is the range of values for the independent variable? ___________________
b. What are the units of the independent variable?
c. What is the control used in this experiment? What is the purpose of the control?
4. Name the dependent variable in the experiment and the units:___________________________
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Unit 1. Experimental Design
Name _______________________
Period________________
5. Looking at the description of what is involved in the experiment, list 4 or 5 pieces of laboratory
equipment that you would need to set up this experiment and describe how it was used.
Equipment
What was it used to do?
1.
2.
3.
4.
5.
The results of this experiment are shown below:
Five of the ten tubes are shown in the picture to the left.
Think about the information you need to record. You want a
single table that includes the following information:
a.
b.
c.
d.
e.
The setting and units for the independent variable
The measure and units for the dependent variable
Repeated measures within a single experiment
Averages, differences, or other calculated values
Repeats of the experiment (Trial 1, Trial 2, Trial 3, etc.)
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Unit 1. Experimental Design
Name _______________________
Period________________
Activity 5. Recording Results
Recording your results clearly and accurately is a very important in an experiment. Analyzing and understanding
your data is easier when you have recorded your results accurately and in an organized way.
This organization requires pre-planning. Understanding the design of your experiment and what data you need
to record means that you can design a table that is easy for you and others to understand. Not only are there
rows and columns for your measurements or observations (raw data), there are also rows or columns for
summary (calculated or “transformed” values.
Let’s look at a table that was created to collect and summarize data from an experiment whose set up is similar
to our catalase experiment. This is an investigation looking at the growth of plants at three pH levels.
Dependent variable
and its units. Trials are
repeated experiments
Space for 2
plants at each
pH
The range of
values for the
independent
variable are in
this column
pH 3
pH 5
pH 7
Plant #
1
2
Mean
3
4
Mean
5
6
Mean
Trial 1 – mass (g)
Days of growth
0
5
10
0.5
1.4
0.6
1.4
0.55 1.4
0.6
2.4
0.8
2.6
0.7
2.5
0.7
2.3
0.6
2.1
0.65 2.2
Trial 2 (mass in g)
Days of growth
0
5
10
Trial 3 (mass in g)
Days of growth
0
5
10
Recorded dependent
variable
Space for calculated
(transformed) data- the
mean
1. Return to the previous case study (catalase activity) and design a table to hold the data. (Use another
sheet of paper). You first must determine the features of your experiment (see below) and it is best to
sketch out a rough copy first. Use the table above as a starting point to design your own table.
a. Independent and dependent variables
b. The levels of the independent variable (in this example the levels are pH 3, 5, and 7)
c. Whether or not you are using a control (think about the “job” of the control in your experiment
d. The number of observations within each level of the independent variable
e. Whether or not you will be calculating values that should be a part of the table
f. Whether multiple observations over time (or a starting value) need to be recorded
g. How many times you are repeating the whole experiment (trials)
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Unit 1. Experimental Design
Name _______________________
Period________________
MORE PRACTICE:
Case study 2. Carbon dioxide levels in a respiration chamber.
A data logger was used to monitor the concentrations
of carbon dioxide (CO2) in respiration chambers
containing five green leaves from one plant species.
The entire study was performed in conditions of full
light and involved three identical set-ups. The CO2
concentrations were measured every minute, over a
period of ten minutes, using a CO2 sensor. A mean
CO2 concentration (for the three set-ups) was
calculated. The study was carried out two more times,
two days apart.
What are your dependent ___________________ and independent variables? ___________________
What is the range of values for the independent variable and how many different values are you measuring?
Is there a control? _________
If not, what would you use as a control?
How many observations (measures) will you make (record) for each of the respiration chambers?
How many respiration chambers are present? Do these chambers represent replication within an experiment or
a different experiment? Explain why.
How many times are you repeating the experiment?
Use the back of the page to sketch out an appropriate graph.
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Unit 1. Experimental Design
Name _______________________
Period________________
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Unit 1. Experimental Design
Name _______________________
Period________________
Activity 6. Transforming the Data (Calculations and Summarizations)
Key Idea: Unprocessed data is called raw data. A set of data is often processed or transformed to make it easier
to understand and to identify trends or patterns in the data. Percentages, rates, frequencies, and counts are
commonly used transformations.
Raw data collected when observing or when doing a controlled experiment often needs to be processed or
transformed into a form that makes it easier to identify patterns or trends in the data. Transformations include
totals, tally charts, means, medians percentages and rates. Examples of each of these transformations is shown
below
Tally sheet
Records the number of times
a value occurs in a dataset.
Example: Height of 6 day old seedlings
Height range
Tally
Total
< 1 cm
|||
3
1 - 1.99 cm
||||
5
2 – 2.99 cm
|||| |||
8
3 – 3.99 cm
|||| |||| ||||
15
4 – 4.99 cm
||||
4
5 – 5.99 cm
||
2
Total your tally for each range.
Your total is the count of plants that
fall into each of your height
categories. This type of chart can
also be called a frequency chart.
If we graph this data, the count
within each height range would be
your dependent variable, while the
height ranges would be your X or
independent variable
Frequency of seedling heights at 6 days
20
Count
15
10
5
0
1
< 0.99
2
1-1.99
3
2- 2.99
4
3-3.99
5
4-4.99
6
5-5.99
Height in cm
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Unit 1. Experimental Design
Name _______________________
Percentages
Period________________
Expressed as a fraction of 100. They
express what proportion of data fall into
any one category
Men
Body mass
(kg)
Lean body
mass (kg)
Athlete
Lean male (nonathlete
Normal weight
male (nonathlete)
Overweight male
Obese male
70
68
60
56
% lean body
mass
(C3/C2)* 100
85.7%
82.3%
83
65
78.3%
96
125
62
65
64.6%
52.0%
In this example, you would need to use a bar graph
to compare differences in lean body mass. It is
important to use percentages, and not the raw data
because each individual has a different body mass,
and the lean must be reported as a fraction of the
“whole” for each person in the study
In other instances (such as the percentage of each
type of bird that feeds in my backyard), you have
category percentages that would sum to 100%. In
this case, pie charts can be used to represent your
data.
Rates
Expressed as a measure per unit time
Rates show how a variable changes over a standard
time period. Notice how the rate changes
depending on the time period).
Rates allow you to compare data that may have
been recorded over different time periods.
Rate of sweat loss during cycling
Time
(minutes)
0
10
20
30
60
Cumulative
sweat loss
(ml)
0
50
130
220
560
Rate of sweat
loss (ml/min)
0
5
8
9
11.3
Questions:
Differentiate between raw data and transformed data. Provide at least two reasons to transform or process raw
data.
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Unit 1. Experimental Design
Name _______________________
Period________________
Data Transformation Practice Problems
Problem 1. Clover is an amazing plant. It is a high quality component of livestock feed (and if you eat meat, you
should care about this) because it is a legume. It fixes nitrogen, which can, in turn, be used by animals to grow
and produce muscle. It can also put nitrogen back into the soil (while it’s alive) which makes it a renewable
“living” mulch. It is often planted between rows of irrigated vegetables, fruit bushes or trees. Because white
clover has “tough stems” and a shallow root system it holds up well to being trampled and mowed (which is
what happens in areas that heavily grazed). Red clover is not as tolerant of heavy traffic, and grows better in
pastures that are not frequently grazed or in wildlife areas. This experimenter went to two different regions to
assess the ratio of red to white clover in each area. One area was frost-free, while the other area was frost
prone. The data are shown below.
Clover type
Frost free area
Frost prone area
Number
%
Number
Red
124
78
26
White
35
Total
159
Show examples of work here:
%
115
1. Complete the calculations for the table above. All boxes should be filled in. You must show examples of
work for your calculations in the box above. An example is shown below. All answers should have
units.
Example: 124 Red÷ 159 total clover plants (Red and White) = 0.78 x 100 = 78%
2. Based on the data gathered, what do you conclude? Given what you have been told in the introductory
paragraph, what factors may not be constant or controlled in the two areas?
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Unit 1. Experimental Design
Name _______________________
Period________________
Transpiration is the evaporation of water from the leaves of plants. Water is lost from the leaf through special
pores called stomata. Stomata are found on both surfaces of the leaf but there are usually more on the ventral
(lower surface) of the leaf. This is to reduce the amount of transpiration that will occur because the top of the
leaf is exposed to more sunlight than the bottom.
The diagram below shows a potometer. A potometer measures the rate of transpiration by measuring the
movement of water into a plant.
Show examples of work here:
Plant water loss using a bubble potometer
Time (min)
0
5
10
15
20
Water displacement in
capillary tube (cm3)
9.0
8.0
7.2
6.2
4.9
Plant water loss
Cm3/min
-0.2
1. Complete the calculations for the table above. All boxes should be filled in. You must show examples of
work for your calculations in the box above. An example is shown below. All answers should have
units.
Example: 9.0 – 8.0 = 1 cm3. 1cm3 ÷5 min = 0.2 cm3/min
2. In our previous example with rates (sweat lost) we saw that the rate of loss increased over time. Do you
see the same trend for the transpiration data above? Can you provide a possible reason? (think about
activity levels of each organism).
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Unit 1. Experimental Design
Name _______________________
Period________________
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