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Analysis checklist: Friedman's test

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Friedman's test is a nonparametric test that compares three or more paired groups.

Was the matching effective?

The whole point of using a repeated-measures test is to control for experimental variability. Some factors you don't control in the experiment will affect all the measurements from one subject equally, so they will not affect the difference between the measurements in that subject. By analyzing only the differences, therefore, a matched test controls for some of the sources of scatter.

The matching should be part of the experimental design and not something you do after collecting data. Prism does not test the adequacy of matching with the Friedman test.

Are the subjects (rows) independent?

The results of a Friedman test only make sense when the subjects (rows) are independent – that no random factor has affected values in more than one row. Prism cannot test this assumption. You must think about the experimental design. For example, the errors are not independent if you have six rows of data obtained from three animals in duplicate. In this case, some random factor may cause all the values from one animal to be high or low. Since this factor would affect two of the rows (but not the other four), the rows are not independent.

Are the data clearly sampled from non-Gaussian populations?

By selecting a nonparametric test, you have avoided assuming that the data were sampled from Gaussian distributions, but there are drawbacks to using a nonparametric test. If the populations really are Gaussian, the nonparametric tests have less power (are less likely to give you a small P value), especially with small sample sizes. Furthermore, Prism (along with most other programs) does not calculate confidence intervals when calculating nonparametric tests. If the distribution is clearly not bell-shaped, consider transforming the values (perhaps to logs or reciprocals) to create a Gaussian distribution and then using repeated-measures ANOVA.

Is there only one factor?

One-way ANOVA compares three or more groups defined by one factor. For example, you might compare a control group, with a drug treatment group and a group treated with drug plus antagonist. Or you might compare a control group with five different drug treatments.

Some experiments involve more than one factor. For example, you might compare three different drugs in men and women. There are two factors in that experiment: drug treatment and gender. Similarly, there are two factors if you wish to compare the effect of drug treatment at several time points. These data need to be analyzed by two-way ANOVA, also called two-factor ANOVA.

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