This document discusses key concepts in experimental design including: - Experiments actively impose treatments to observe responses while observational studies simply observe outcomes. - Proper experimental design includes random assignment of experimental units to control and treatment groups to control for confounding variables and ensure validity. - Examples demonstrate different experimental designs like completely randomized and randomized block designs. Well-designed experiments are needed to establish cause and effect.

1. Chapter 2Chapter 2
Experimental
Design
By Kathy Fritz
Plano West High School

2. Definitions:
1) Observational study -
observe outcomes without
imposing any treatment
2) Experiment - actively impose
some treatment in order to
observe the response

3. I’ve developed a new rabbit food, Hippity
Hop.
Rabbit Food
Makes fur
soft &
shiny!
Increases
energy!
100% of daily
vitamins &
essential oils!

4. Can I just make these claims?
What must I do to make these
claims?
Who (what) should I test this
on?
What do I test?
NO
Do an experiment
Rabbits
The type of food

5. 3)Experimental unit – the single
individual (person, animal,
plant, etc.) to which the
different treatments are
assigned
4) Factor – is the explanatory
variable
5) Level – a specific value for
the factor

6. 6) Response variable – what you
measure
7) Treatment – a specific
experimental condition applied to
the units

7. I plan to test my new rabbit
food.
What are my experimental
units?
What is my factor?
What is the response variable?
Rabbits
Type of food
How well they grow

8. Hippity Hop
I’ll use my pet
rabbit, Lucky!
Since Lucky’s coat is shinier &
he has more energy, then
Hippity Hop is a better rabbit
food!

9. 8) Control group – a group that
is used to compare the factor
against; can be a placebo or the
“old” or current item
9) Placebo – a “dummy”
treatment that can have no
physical effect

10. Old Food Hippity Hop
Now I’ll use Lucky & my
friend’s rabbit, Flash.
Lucky gets Hippity Hop
food & Flash gets the
old rabbit food.
WOW! Lucky is bigger &
shinier so Hippity Hop is
better!

11. Old Food Hippity Hop
The first five rabbits
that I catch will get
Hippity Hop food and
the remaining five will
get the old food.
The Hippity Hop rabbits have
scored higher so it’s the better
food!

12. Old Food Hippity Hop
Number the rabbits from 1 –
10.
Place the numbers in a hat.
3
5 7
8
9
41
2 6
10
I evaluated the rabbits & found
that the rabbits eating Hippity
Hop are better than the old
food!
39857
9
7
5
8
3
6
1 2 10
4
The first five numbers
pulled from the hat will be
the rabbits that get
Hippity Hop food.
The remaining rabbits get
the old food.

13. 10) blinding - method used so
that units do not know which
treatment they are getting
11) double blind - neither the
units nor the evaluator know
which treatment a subject
received

14. Rabbit Food
Hippity Hop
Rabbit Food
makes fur soft
and shiny, &
increases
energy for ALL
types of
rabbits!
Can I
make this
claim?

15. Principles of ExperimentalPrinciples of Experimental
DesignDesign
• Control of effects of extraneous
variables on the response – by
comparing treatment groups to a
control group (placebo or “old”)
• Replication of the experiment on
many subjects to quantify the
natural variation in the experiment
• Randomization – the use of chance
to assign subjects to treatments

16. The ONLY way to show cause &
effect is with a well-designed,
well-controlled experiment!
The ONLY way to show cause &
effect is with a well-designed,
well-controlled experiment!!
The ONLY way to show cause
& effect is with a well-
designed, well-controlled
experiment!!!

17. Example 1: A farm-product manufacturer wants
to determine if the yield of a crop is different
when the soil is treated with three different
types of fertilizers. Fifteen similar plots of
land are planted with the same type of seed
but are fertilized differently. At the end of
the growing season, the mean yield from the
sample plots is compared.
Experimental units?
Factors?
Levels?
Response variable?
How many treatments?
Plots of land
Type of fertilizer
Fertilizer types A, B, & C
Yield of crop
3

18. Example 2: A consumer group wants to
test cake pans to see which works the
best (bakes evenly). It will test aluminum,
glass, and plastic pans in both gas and
electric ovens.
Experiment units?
Factors?
Levels?
Response variable?
Number of treatments?
Two factors - type of pan & type of oven
Type of pan has 3 levels (aluminum, glass, & plastic
& type of oven has 2 levels (electric & gas)
How evenly the cake bakes
6
Cake batter

19. Example 3: A farm-product manufacturer
wants to determine if the yield of a crop is
different when the soil is treated with three
different types of fertilizers. Fifteen similar
plots of land are planted with the same type
of seed but are fertilized differently. At the
end of the growing season, the mean yield
from the sample plots is compared.
Why is the same type of seed used on all 15
plots?
What are other potential extraneous
variables?
Does this experiment have a placebo? Explain
It is part of the controls in the experiment.
Type of soil, amount of water, etc.
NO – a placebo is not needed in this experiment

20. Experiment DesignsExperiment Designs
• Completely randomized – all
experimental units are
allocated at random among all
treatments
Treatment group 1
Treatment group 2
Treatment group 3
explanatory
variable
response
variable
Randomassignment

21. Treatment C
Treatment B
Randomly assign
experimental units to
treatments
Treatment A
Treatment D
Completely randomized design

22. • Randomized block – units are
blocked into groups and then
randomly assigned to
treatments
Randomassignment
Group1
Group2
Treatment 1
Treatment 2
Treatment 3
Treatment 3
Treatment 2
Treatment 1
explanatory
response
varaible varaible

23. Treatment B
Randomly assign
experimental units to
treatments
Treatment A
Put into homogeneous
groups
Treatment A Treatment B
Randomized block design

24. – match up experimental units
according to similar
characteristics & randomly assign
one to one treatment & the other
automatically gets the 2nd
treatment
– have each unit do both
treatments in random order
– the assignment of treatments is
dependent
•Matched pairs - a specialMatched pairs - a special
type of block designtype of block design

25. Pair experimental
units according to
specific
characteristics.
Next, randomly assign
one unit from a pair
to Treatment A. The
other unit gets
Treatment B.
Treatment A Treatment B
This is one way to do a matched
pairs design – another way is to have
the individual unit do both
treatments (as in a taste test).

26. 12) Confounding variable – the
effect of the confounding
variable on the response cannot
be separated from the effects of
the explanatory variable (factor)

27. Suppose we wish to test a new
deodorant against one currently on
the market.

28. Treatment BTreatment A
Treatment A Treatment B
One group is
assigned to
treatment A & the
other group to
treatment B.
Treatment & group are confounded
ConfoundingConfounding does NOT
occur in a completely
randomized design!

29. Example 4: An article from USA
Today reports the number of victims
of violent crimes per 1000 people. 51
victims have never been married, 42
are divorced or separated, 13 are
married, and 8 are widowed.
Is this an experiment? Why or why
not?
What is a potential confounding
variable? Age – younger people are more at risk
to be victims of violent crimes
No, no treatment was imposed on people.

30. Example 5: Four new word-processing
programs are to be compared by measuring
the speed with which standard tasks can
be completed. One hundred volunteers are
randomly assigned to one of the four
programs and their speeds are measured.
Is this an experiment? Why or why not?
What type of design is this?
Factors? Levels?
Response variable?
Yes, a treatment is imposed.
Completely randomized
one factor: word-processing
program with 4 levels
speed

31. Example 5: Four new word-processing
programs are to be compared by
measuring the speed with which
standard tasks can be completed.
One hundred volunteers are randomly
assigned to one of the four programs
and their speeds are measured.
Is there a potential confounding
variable? Can this design
be improved?
Explain.
NO, completely randomized
designs have no confounding
You could do a block
design where each person
uses each program in
random order.

32. Example 6: Suppose that the manufacturer
wants to test a new fertilizer against the
current one on the market. Ten 2-acre plots of
land scattered throughout the county are used.
Each plot is subdivided into two subplots, one
of which is treated with the current fertilizer,
and the other with the new fertilizer. Wheat
is planted and the crop yields are measured.
What type of design is
this? Why use this
method?
When does
randomization occur?
Matched - pairs
design
Randomly assigned
treatment to first acre
of each two-acre plot

33. Randomization reduces bias by
spreading any uncontrolled
confounding variables evenly
throughout the treatment groups.
Variability is controlled by sample
size. Larger samples produce
statistics with less variability.
Blocking also helps reduce
variability.
Is there another way
to reduce variability?

34. High bias & low variability
Low bias & low variabilityLow bias & high variability
High bias & high variability