Choosing a one-way ANOVA

Prism offers four related tests that compare three or more groups. Your choice of a test depends on two choices:

Repeated measures?

Choose a repeated measures test when the columns of data are matched. Here are some examples:

•	You measure a variable in each subject several times, perhaps before, during and after an intervention.

•	You recruit subjects as matched groups, matched for variables such as age, ethnic group, and disease severity.

•	You run a laboratory experiment several times, each time with several treatments handled in parallel. Since you anticipate experiment-to-experiment variability, you want to analyze the data in such a way that each experiment is treated as a matched set.

Matching should not be based on the variable you are comparing. If you are comparing blood pressures in three groups, it is OK to match based on age or zip code, but it is not OK to match based on blood pressure.

The term repeated measures applies strictly when you give treatments repeatedly to one subject (the first example above). The other two examples are called randomized block experiments (each set of subjects is called a block, and you randomly assign treatments within each block). The analyses are identical for repeated measures and randomized block experiments, and Prism always uses the term repeated measures.

Nonparametric test?

Nonparametric tests, unlike ANOVA are not based on the assumption that the data are sampled from a Gaussian distribution. But nonparametric tests have less power, and report only P values but not confidence intervals. Deciding when to use a nonparametric test is not straightforward.

Test summary

Test	Matched	Nonparametric
Ordinary one-way ANOVA	No	No
Repeated measures one-way ANOVA	Yes	No
Kruskal-Wallis test	No	Yes
Friedman test	Yes	Yes