How to do Science Well: Your Handy Guide to Getting it Right
Experimental Design Vocabulary, Key Ideas, and Skills
 Variable: Something that may change (vary).
Suppose you wanted to know what the relationship was between the amount of fertilizer used
and how tall a plant would grow?
There are Three Types of Variables:

Manipulated Variable (MV): The manipulated variable is the one thing you change on
purpose. When you manipulate this variable you are looking for a response or an
effect in the responding variable.
In the example, the MV would be the amount of fertilizer used.

Responding Variable (RV): The responding variable is the thing that changes (the
thing that is effected) because the manipulated variable was changed.
In the example, the RV would be how high the plants grow.

Controlled Variables (CV): The controlled variables are the things you keep the
same every time so that you can be sure that the only thing that changed in your set
up, from one trial to the next, was the variable you manipulated on purpose, the MV.
In the example, CVs would include the amount of water the plants are given,
the size of the pots they are grown in, the temperature of the air around each
pot, and lots more.
EXPERIMENTAL CONTROL (EC): The set of trials in your experiment where you
don’t apply the manipulated variable at all.
Generally, the experimental control (sometimes called the control group) serves as the
standard of comparison so you can know what "normal" is. More importantly, having a proper
experimental control allows you to know for certain that the effect you measured (the change
in the RV) was in fact caused by the one thing you manipulated (the change in the MV).
In the example, the EC would be the plant(s) that don't get any fertilizer at all. Let's say
you are working with corn plants. You add various amounts of fertilizer and find that
the plants grow 5 centimeters more if you add a lot of fertilizer than if you only add a
little. Adding lots of fertilizer is the way to go, right? Not necessarily. If you had an
experimental control you would know that without the fertilizer at all the corn plants
would grow really tall anyway! Adding a lot of expensive fertilizer would be a waste of
money, it hardly makes any difference. But, you wouldn't have known that if you didn't
have an experimental control, you just would have thought, “Wow, this fertilizer makes
the plants grow really tall!”
Levels of the Manipulated Variable: The different levels (amounts) of the
manipulated variable that should be tested.
The different levels of the manipulated variable should be evenly spaced and cover the range
of what is reasonable and possible. Test a minimum of 3 different levels. If you test fewer
than 3 levels it is difficult to know what the real relationship is between the MV and RV. With
fewer than 3 data points a graph of the data always looks linear, even when it shouldn’t.
Let’s say the manufacturer of the fertilizer recommends that you use 2.5 grams per
plant per week. The levels of the MV might then be 0g per week of fertilizer (the CG),
1g per week, 2g per week, 3g per week, 4g per week, and 5g per week. This way
there would be at least 3 levels (actually there are 5 here!), they would be evenly
spaced, and they would cover the range from none at all to an amount that probably is
way too much.
Repeated Trials: Testing each level of the manipulated variable more than once to
ensure accuracy.
We repeat trials to make sure our results are not a fluke and that our procedure is reliable.
Each level of MV should be tested at least three times. The three (or more) pieces of data
can then be averaged. If the data gathered is similar each time you do a particular level of the
MV, that suggests that your procedure is reliable and you can trust that your data didn't turn
out the way it did due to random error or chance.
In the example, we would test each level at least three times. If for the 3g trials my
data turned out to be 240cm, 247cm and 242cm, I could be confident that my
procedure is reliable since the data points are so close together. I would average them
and say that when 3g per week of fertilizer is added, corn plants grow to a height of
243cm. If, however, my data turned out to be 130cm, 223cm and 290cm, I would
recognize that something is wrong with my experiment. The data is all over the place
and I really don't know with confidence how tall corn plants grow when 3g per week of
fertilizer is used. I would need to rethink whether I really controlled all of the other
variables that I should have.
Reliability: The degree to which you can be confident that the data you collected
didn't turn out the way it did due to random error or chance.
Reliability is about how confident you are in your data. The procedure you design should be
good enough that each time you conduct a trial at a particular level of the MV you get about
the same answer. If you don't, you can't trust your data and need to rethink your procedure
and whether you are controlling all of the extraneous variables. The way you test to see if you
procedure and data are reliable is to do repeated trials. If you keep getting about the same
answer you can be confident that your data is accurate, that your procedure produces reliable
data. To make sure that your data will be reliable, design a good procedure and control all
extraneous variables. To find out if your data is reliable, repeat trials and see if you get about
the same answer each time.
In the example we would have controlled for everything we could think of. We would
have even made sure the seeds we used came from the same parent plant! Then, we
would have tested each level multiple times to see if the data came out the same each
time.
Validity: The degree to which we can be confident that the change measured in the
responding variable really was caused by the change in the manipulated variable and
not by something else.
Validity is about how confident you are in that you have isolated the relationship between the
MV and the RV and thus your conclusion is the right one. If you did a really good job of
isolating the manipulated variable (controlling all of the other variables) and you included a
control group, then you can be fairly confident that your results, and thus your conclusion, is
valid; the change in the responding variable really was caused by the change in the
manipulated variable. To ensure that your conclusion will be valid, you control all extraneous
variables and include a control group in your experiment. To ensure that your conclusion is
valid, that you really do understand the relationship between the manipulated and responding
variable correctly, you check with other scientists to see if they also discovered the
relationship you did. Another way to improve you confidence in the validity of your conclusion
is to conduct a different experiment where you test the same relationship in a different way.
In the example, we would have included a control group so that we would know with
certainty that if the plants with more fertilizer grew taller than those with less it was
because of the fertilizer and not some other variable we didn't think to control. We
might also look into what other researchers investigating the same question have
discovered.
Graphing: The style of graph that should be used to report the results of an
experiment depends upon whether the manipulated variable measures something that
is discrete or continuous.
Discrete variables come in different types. With discrete variables, there are no in between
values. Examples include, dog breeds and makes of car. For this type of variable a bar graph
should be used. The manipulated variable belongs on the horizontal (x) axis and the
responding variable belongs on the vertical (y) axis. The graph should have a title and be well
labeled, including labels for each axis and the units used to measure each variable.
Continuous variables have values in between the ones you use in your experiment’s
procedure. Examples include temperature, length, and time. For this type of variable, a line
graph should be used. The manipulated variable belongs on the horizontal (x) axis and the
responding variable belongs on the vertical (y) axis. Generally speaking, a best-fit line should
be drawn that flows along the plotted data points. The graph should have a title and be well
labeled, including labels for each axis and the units used to measure each variable.
In the example, we would have used a line graph to report our results. We used levels
of the MV that were 0, 1, 2, 3, 4 and 5g of fertilizer. Our results should allow us to infer
how tall a plant would grow if we used 1.5g of fertilizer or perhaps even 5.5g.
How to Write an Exemplary Lab Report
Below you will find nearly everything you need to know about how to write an exemplary lab
report at the high school level. The well formatted report would include eight, labeled sections
plus the title of the report at the top of the first page or on a cover page. It would of course be
typed. It should be free of typographical and spelling errors. The writing should be clear,
concise, and in a professional tone.
1. Title
The title of your investigation should be phrased in terms of the relationship between the
manipulated variable and the responding variable. Generally, it is a statement about the
effect of the manipulated variable on the responding variable in the specific system being
investigated.
In the example, the title might be, "The effect of fertilizer on the mature height of Zea
saccharata (sweet corn)."
Or the title can reflect the title as written in your textbook.
2. Investigative Question
The investigative question section is where you write out the question you are trying to
answer by conducting your investigation.
In the example, the question might be, "What is the relationship between the amount
of fertilizer used and the mature height of Zea saccharata?"
It may seem silly on first glance to include a title and a question which are essentially saying
the same thing. But, it does make sense. You need a title so that there will be something to
put in the table of contents when your investigation is published. Heading your experiment
with a question makes sense since; after all, answering questions is what science is all about.
Make sure that your IQ is scientifically testable.
4. Hypothesis
In the hypothesis section you write what you predict the answer to the experimental question
will be. The hypothesis statement should be a single sentence in the format of an if, then,
because statement. If you change the manipulated variable in a particular way, then the
responding variable will change in this other particular way because of some reason which
you then identify.
In the example, the hypothesis might be, “If the amount of fertilizer used on Zea
saccharata plants in increased, the height to which the plants grow will increase until a
point is reached at which adding even more fertilizer will make them grow less high
because plants need the nutrients in fertilizer to grow well, but can be “burned” if too
much fertilizer is added.”
It can be easy to forget to state in what way the manipulated variable will change and in what
way you expect the responding variable will change. It would not be acceptable to just say, “If
the amount of fertilizer is changed then the height to which the plants grow will change.” Your
job isn’t just to find out if a relationship exists between the MV and the RV your job is to find
out what that relationship is.
5. Materials
In the materials section you make a list of all of the items that would be needed if someone
else were attempting to repeat your experiment. Your list should be exactly that, just a list.
Your descriptions of the items on the list need to be specific enough that someone who didn’t
see what you did would know exactly what was needed and even how much of each thing
was needed.
In the example, the materials list would include more than just the following:
18 Zea saccharata seeds
18 400 mL beakers
1 4 foot, fluorescent grow light (60 watt)
1 8oz. bottle of liquid Miracle Gro® Brand Indoor Plant Fertilizer
1 0.01g balance
6. Procedure
The procedure section should read like a detailed list of instructions on what someone should
do to exactly repeat the experiment you conducted. The list should be numbered and in
complete sentences. The instructions should be detailed enough so that someone who did
not witness you doing the experiment would know exactly what to do. It is always a good idea
to include a diagram of your experimental set-up in this section. Be sure to be specific about
what levels of the manipulated variable are to be tested and how many trials should be done
for each. It should also be clear what you did to control each of the controlled variables. The
first steps listed in any procedure should be about any safety precautions that ought to be
taken.
For our example the procedure might start out like this:
1. Always wash your hands after handling fertilizer.
2. Put 400 mL of soil into each of 18 beakers.
3. In the center of each make a 3 cm depression with your finger. Place one
seed into the bottom of each depression and then smooth the soil over
so that the seed is covered and the surface of the soil is smooth.
4. Beakers 1-3 will serve as the control group. Gently pour 100 mL of water
into each beaker.
5. Beakers 4-6 will each receive 1 g of fertilizer. Add 1 g of fertilizer to 100
mL of water. Stir until the fertilizer has dissolved. Gently pour the fertilizer
solution into beaker number 4. Repeat this procedure for beakers 5-6.
6. Beakers 7-9 will each receive 2 g of fertilizer…
7. Data
The data section is for raw data and observations only. The data should be recorded in a
clearly labeled table. The table should have a title. Each column and row should have a label
and the units associated with all of the numbers reported should be clear. You should record
observations made during the experiment in a section of your data table dedicated just to
observations. The impact from an observation may not be immediately known, but may aid in
your error analysis and explanations in your conclusion.
For our example the data table might look like this (only a portion is shown):
Amount of Fertilizer vs. Height to which Corn Plants Grow
Beaker
#
Amount of
Fertilizer
Added (g)
1
0
2
0
3
0
4
1
5
1
6
1
7
2
8
2
Plant Height After
30 Days Growing
Time (cm)
Observations
8. Results
The results section is where you report the processed data. Usually, this will look like a table
with the averaged raw data, a graph of the averaged data, or both. In addition to a table
and/or graph, there should be a brief paragraph identifying any trends seen in the processed
data. As well, this would be the place where you would want to note if any of the data points
were not included in the calculation of an average for a particular level of the manipulated
variable and why. It may be that the data from one trial was very different from the other data
points for that level of the manipulated variable and you decided not to trust it. It may be that
a particular trial failed for some reason. In any case, this is where you would explain why
particular data points were discounted.
For our example the results table might look like this (only a portion is shown):
Amount of Fertilizer vs. Average Height to which Corn Plants Grow
Amount
of
Fertilizer
Added
(g)
0
1
2
3
Average Plant
Height After 30
Days Growing
Time (cm)
Notes
9. Conclusion
Here is where you get to interpret your results. Do they support or refute your hypothesis?
How do they compare with other information on the same topic? You may include your
opinions; do not be afraid to admit where you may have made mistakes. If you can think of
any ways to improve your experiment, or if you think of a new hypothesis you would like to
test, you can include those here, too. Be sure to discuss CORRELATIONS (when things tend
to happen together) and CAUSE and EFFECT (when one thing causes another to occur).
Make sure to CITE ACTUAL DATA. The conclusion is a summary of your experiment.
You can write a good conclusion by writing a paragraph or paragraphs that answer the five
sets of questions below in detail. Notice that some of your answers repeat other parts of your
report. That is OK. This will enable someone to understand your whole experiment just by
reading the conclusion.
1. What was your hypothesis for the investigation?
2. Did the results of your investigation support your hypothesis or contradict it?
3. How did your findings compare with other scientists’ research on the same topic, or
information in your own background research? Explain in depth and detail.
4. What possible explanation can you give for your findings? Give detailed reasoning
for each explanation. How confident are you in the reliability of your procedure? What
evidence do you have to back that up?
5. How confident are you in the validity of your conclusion. What evidence do you have
to back that up? In what ways could you improve your experiment? How might this
impact your results? What further questions might you ask or research might you do
on the topic?
10. Comment
In this section please reflect on the lab. What did you really like, and why? What could have
been better….why? What was confusion? Did you have any “ah ha” moments? Could you
share??