Why should I use a Friedman test?

To perform this test, select Stat > Nonparametrics > Friedman.

Use Friedman test to determine whether treatment effects differ in a randomized block design experiment when you have data that are not necessarily symmetric.

For example, a marketing company wants to compare the relative effectiveness of three different modes of advertising: direct mail, newspaper, and magazine advertisements. The company conducts a randomized block design experiment. For 14 customers, the marketing company used all 3 modes during a 1-year period and recorded the percentage response to each type of advertising.

Minitab prints the test statistic, which has an approximately chi-square distribution, and the associated degrees of freedom (number of treatments minus one). If there are ties within one or more blocks, the average rank is used, and a test statistic corrected for ties is also printed. If there are many ties, the uncorrected test statistic is conservative; the corrected version is usually closer, but can be either conservative or large. Minitab displays an estimated median for each treatment level. The estimated median is the grand median plus the treatment effect.

For Friedman test, the hypotheses are:
  • H0: all treatment effects are zero
  • H1: not all treatment effects are zero
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