Review
Elements of a Good Experiment
• Control comparison group
control group, placebo effect
• Randomizing in an experiment
◦ to eliminate bias that may result if the researchers assign
the subjects
◦ to balance the groups on variables that the researchers
know affect the response
◦ to balance the groups on lurking variables that may be
unkonw to the researchers
• Blinding the study
double-blind
• ordinary random variation.
• statistical significance
When the difference between the results for the two
treatments is so large that it would be rare to see such a
difference by ordinary random variation, we say that the
results are statistically significant.
Key Parts of a Good Experiment
• A good experiment has a control comparison group,
randomization in assigning experimental units to treatments,
blinding, and replication.
• The treatments are the experimental conditions imposed on
the experimental units. The treatments correspond to values
of an explanatory variable.
• Randomize in assigning the experimental units to the
treatments. This tends to balance the comparison groups with
respect to lurking variables.
• Replicating the treatments on many experimental units
helps, so that observed effects are not due to ordinary
variability but instead are due to the treatment. Repeat
studies to increase confidence in the conclusions.
Other Random Sampling Designs (other than Simple Random
Sampling)
• Cluster Random Sampling
Divide the population into a large number of clusters. Select a
simple random sample of the clusters. Use the subjects in
those clusters as the sample. This is a cluster random
sample.
• Stratified Random Sampling
A stratified random sample divides the population into
separate groups, called strata, and then selects a simple
random sample from each stratum.
In practice, more than one sampling methods can be applied.
Retrospective Observational Studies (backward looking)
• Case-Control Study
A case-control study is a retrospective observational study in
which subjects who have a response outcome of interest (the
cases) and subjects who have the other response outcome
(the controls) are compared on an explanatory variable.
Prospective Observational Studies (forward looking)
Multifactor Experiments
• Categorical explanatory variables in an experiment are often
referred to as factors.
Matched Pairs Designs
• help to keep potential lurking variables from affecting the
results.
Blocking Designs
• to reduce possible bias, treatments are usually randomly
assigned within a block.