participants nor the doctors knew who was actually
receiving green tea
After one year, only 1 person taking green tea had
gotten cancer, while 9 taking the placebo had gotten
cancer

Green Tea and Prostate Cancer
A difference this large is unlikely to happen just
by random chance.
Can we conclude that green
tea really does help prevent prostate cancer?

Statistics: Unlocking the Power of Data
Lock
5

Statistics: Unlocking the Power of Data
Lock
5
Types of Randomized Experiments
Randomizing cases into different treatment
groups is called a
randomized comparative
experiment
We can also give each treatment to each case,
and just randomize the
order
in which
treatments are received:
matched pairs
experiment
Either are valid randomized experiments!

Statistics: Unlocking the Power of Data
Lock
5
Type of Randomization
Run an experiment using 30 right-handed people to determine whether
gripping strength is greater in the dominant hand.
a) Randomized comparative design – randomly divide the 30 people into two
groups of 15 each
b) Matched pairs design – randomize the order by selecting 15 to first use the
tight hand and the other 15 to first use the left and examine the difference
between the two values.

Statistics: Unlocking the Power of Data
Lock
5
Was the sample
randomly selected?
Possible to
generalize to
the population
Yes
Should not
generalize to
the
population
No
Was the explanatory
variable randomly
assigned?
Possible to
make
conclusions
about causality
Yes
Can not make
conclusions
about causality
No
Randomization in Data Collection

Statistics: Unlocking the Power of Data
Lock
5
DATA
Two Fundamental Questions in
Data Collection
Population
Sample
Random
sample???
Randomized
experiment???

Statistics: Unlocking the Power of Data
Lock
5
Randomization
Doing a randomized experiment on a random
sample is ideal, but rarely achievable
If the focus of the study is using a sample to
estimate a statistic for the entire population, you
need a random sample, but do not need a
randomized experiment (example: election polling)
If the focus of the study is establishing causality
from one variable to another, you need a
randomized experiment and can settle for a non-
random sample (example: drug testing)

Statistics: Unlocking the Power of Data
Lock
5
Summary
Association does not imply causation!
In observational studies, confounding variables
almost always exist, so causation cannot be
established
Randomized experiments involve randomly
determining the level of the explanatory variable
Randomized experiments prevent confounding
variables, so causality can be inferred
A control or comparison group is necessary
The placebo effect exists, so a placebo and
blinding should be used