Experiment
Imposing treatments on the
subjects in order to
compare the results.
The objects on which the
treatment is imposed on are
called experimental units (human
subjects).
Explanatory variables are called
factors and specific values of the
explanatory variable are levels.
These determine the treatments.
Does the type of lighting or the type of music
in a dentist’s waiting room have any effect on
the anxiety of a patient?
• Types of music:
– pop
– classical
– jazz
• Levels of brightness
– low
– medium
– high
• What are the factors?
Type of music & Brightness level
• How many levels are
there of each factor? 3
• How many treatments
are there?
• What could a possible
response variable be?
Blood pressure, ...
9
Does the increase in the explanatory variable
CAUSE the increase in the response variable?
Year
Number of Methodist Ministers in
New England
Cuban Rum Imported to
Boston (in barrels)
1860
63
8,376
1865
48
6,406
1870
53
7,005
1875
64
8,486
1880
72
9,595
1885
80
10,643
1890
85
11,265
1895
76
10,071
1900
80
10,547
1905
83
11,008
1910
105
13,885
1915
140
18,559
1920
175
23,024
1925
183
24,185
1930
192
25,434
1935
221
29,238
1940
262
34,705
The Beauty of Experiments
Just because two variables have a relationship, that
doesn’t mean one causes the other – there is often
a confounding variable at play!
BUT…long term well-designed experiments CAN
be used to imply CAUSATION between the
explanatory variable and the response variable.
Observational studies/ surveys can NOT!
http://tylervigen.com/
Designing Good Experiments
• Students in Red Group
– Do jumping jacks for two minutes
– Then measure heart rate
• Students in Blue Group
- Jog in place for two minutes
- Then measure heart rate
• Explanatory variable: type of activity (jogging or jj)
• Response variable: heart rate
In which option can we conclude that the change in
the explanatory variable CAUSES the biggest
change in the response variable?
• Option 1: Students choose which group to join
• Option 2: Students are randomly assigned to a group.
3 Principles of Good Experimental Design
Replication--consistency to many subjects
Randomization--randomly assign
subjects to treatment groups
Control/Comparison—having at
least two groups that can be
compared
“Differences in the response variable between groups, if
enough to rule out natural chance variability, can then
be attributed to the manipulation of the explanatory
variable.” This will allow determination of cause and
effect.
Ways to Randomize
• Each subject draws
out of a bag a colored
chip - each treatment
is a different color
• Each subject is
assigned a number first set of random
numbers produced go
to one treatment and
the rest to the other
• Each subject rolls a
die - odds go to one
treatment, evens to the
other
• Each subject flips a
coin - heads go one
treatment, tails to the
other
Other Experimental Vocabulary
Control group--receives standard/traditional
treatment OR no treatment at all OR a
Placebo -receives no active ingredient but
subjects believe they are receiving treatment
Single Blind: subjects don’t know
which treatment they receive
Double Blind: subjects and
evaluators are “blind”; only the
researcher has the “key”
Completely Randomized Design
• Randomly assign a treatment to each experimental
unit
• The number of units assigned to each treatment is
as equal as possible
• Randomization is expected to spread any
differences among units equally across all
treatment groups
• Any significant difference in the two groups’
responses can be attributed to treatments used –
therefore there are no confounding issues
Is weight training good for children? If so, is
it better for them to lift heavy weights for a
few repetitions or moderate weights a larger
number of times? Include:
14--Heavy
load group
43
volunteers
Randomly
assigned to
3 treatment
groups
15--Moderate
load group
14--Control
group – no
weights
•
•
•
•
Subjects
Random assignment
Explanatory(treatments)
Response
Measure and
compare
muscular strength
& endurance
Block Designs to Reduce Variability
Block Design--divide units
into groups (blocks) in Matched-Pair Design—
which the units in each
blocking on a unit:
block are similar to each randomly assign either two
other. Within each block matched units (identical
randomly assign
twins) the treatments OR
treatments (do multiple
the same individual
CRDs).
receives both treatments in
Block if you have reason to random order
believe certain groups
will have different
results.
Blocking reduces variability.
Randomized Block Design
7--Heavy
21-Children
aged
5-10
Randomly
assigned to
3 groups
7--Control
7--Heavy
43
volunteers
blocked
by age
7--Moderate
22-Children
aged
11-16
Measure &
compare
between
groups
Randomly
assigned to
3 groups
8--Moderate
7--Control
Block by age because we believe younger children might have
different results than older children
Measure &
compare
between
groups
Matched Pair Design
43
volunteers
Randomly
assigned to
2 groups
21—Heavy load
for six weeks,
rest two weeks,
moderate load for
six weeks
22 – Moderate
load for six
weeks, rest two
weeks, heavy load
for six weeks
Measure
increases in
strength &
endurance
& compare
between
groups
Quitting Smoking w/Nicotine Patches
Recruited 240 smokers (volunteers) at Mayo Clinic
from 3 large cities
Randomly assigned 22-mg nicotine
patch or placebo patch for 8 weeks.
All attended counseling before, during, and after.
Double-blind
After 8-wk (1 yr), 46% (27.5%) of nicotine patch
group quit smoking and 20% (14.2%) of placebo
group quit.
Quitting Smoking w/Nicotine
Patches
•
•
•
•
•
•
•
•
•
What are the experimental units? • 240 volunteers
What are the treatments?
• Patches
• Type of patch
What was the explanatory variable?
What was the response variable? • Whether or not they quit
• Assigned patch
How was randomization applied? randomly
How was control applied?
• Placebo patch
How was replication applied?
• About 120 in each group
•
Is this an experiment or an observational
study? Experiment
How would you summarize the results of this experiment?
• The nicotine patch
worked!
Which type of experiment?
• A baby-food producer claims that her
product is superior to that of her leading
competitor, in that babies gain weight faster
with her product. As an experiment, 30
healthy babies are randomly selected. For
two months, 15 are fed her product and 15
are feed the competitor’s product. Each
baby’s weight gain (in ounces) was
recorded.
• Completely Randomized Design
• Randomly assign babies to treatments.
Have 15 red and 15 blue chips in a bag and
draw one for each baby. Reds get her
product, blues get the competitor’s product.
• Or …Assign each baby a number 01-30.
Generate 15 distinct random numbers in this
range to get her product. The rest get the
competitor’s product.
• Compare the weight gain of the babies after
the two month period.
Which type of experiment?
• Is the right hand of right-handed people generally
stronger that the left? Paul Murky of Murky
Research designs an experiment to test this
question. He fastens an ordinary bathroom scale to
a shelf five feet from the floor, with the end of the
scale projecting out from the shelf. Subjects
squeeze the scale between their thumb and their
fingers on the top. A scale, which reads in
pounds, will be used to measure hand strength.
• Matched Pairs Design
• Randomly assign ½ of the people to test
right and then left hand, and the other ½ to
test left hand first, and then right hand.
• Make sure the participants cannot read the
scale so they don’t influence themselves
into trying to “top their score”.
• Compare the differences in hand strength.
Which type of experiment?
• An agronomist wishes to compare the yield
of five corn varieties. The field, in which
the experiment will be carried out, increases
in fertility from north to south.
• Block design
• Block the field on
location since fertility
increases from north
to south. (each color is
one block)
• Randomly assign each
plot within the block
to 1 of the 5 corn
varieties (1-5).
• Compare the yield of
corn from each of the
plots of land.
Randomization in ALL Studies
• In Observational
Studies
– Randomize
selection of
subjects
• In Experiments
– Randomize
assignment of
treatment
Using Random number table to randomize
assigning treatments
• 15487 45195 56420 02314 41265 03798 23185 15770
21468 02172 39741 01468 15647 04841 54970 32670
• Suppose you have 3 treatments for 30 subjects
• Option 1: Assign subjects a number and first 10 numbers
chosen get treatment A, second 10 get B and the rest get C.
• Option 2: Assign the treatments a number or range of
numbers (A is 1-3, B is 4-6, C is 7-9, ignore 0) and each
subject gets the next treatment that comes up. Stop when
there are 10 in each treatment group.