The P values are calculated from the ANOVA table. If the P value is high, you can conclude that the matching was not effective and should reconsider your experimental design. If this P value is low, you can conclude that the matching was effective. Prism tests whether the matching was effective and reports a P value that tests the null hypothesis that the population row means are all equal. If the pairing is ineffective, however, the repeated-measures test can be less powerful because it has fewer degrees of freedom. The repeated-measures test is more powerful because it separates between-subject variability from within-subject variability. If the matching is effective, the repeated-measures test will yield a smaller P value than an ordinary ANOVA. Was the matching effective?Ī repeated-measures experimental design can be very powerful, as it controls for factors that cause variability between subjects. Look at the results of post tests to identify where the differences are. This doesn't mean that every mean differs from every other mean, only that at least one differs from the rest. You can reject the idea that all the populations have identical means. If the overall P value is small, then it is unlikely that the differences you observed are due to random sampling. You just don't have compelling evidence that they differ. This is not the same as saying that the true means are the same. Even if the true means were equal, you would not be surprised to find means this far apart just by chance. If the overall P value is large, the data do not give you any reason to conclude that the means differ.

If all the populations really have the same mean (the treatments are ineffective), what is the chance that random sampling would result in means as far apart (or more so) as observed in this experiment? The analyses are identical for repeated-measures and randomized block experiments, and Prism always uses the term repeated-measures. The term repeated-measures strictly applies only when you give treatments repeatedly to each subject, and the term randomized block is used when you randomly assign treatments within each group (block) of matched subjects. Repeated-measures ANOVA compares the means of three or more matched groups.

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