To ensure that your results are valid, consider the following guidelines when you collect data, perform the analysis, and interpret your results.
If you use a parametric analysis as an alternative to the Friedman test, you should verify your data meets the data requirements of that analysis. The data requirements for parametric analyses are not always compatible with the requirements for nonparametric analyses, such as the Friedman test.
- The data should include two categorical factors.
One factor is the treatment. The other factor is the block that each treatment is randomly assigned to. The Friedman test requires exactly one observation for each combination of treatment and block. Minitab cannot complete the calculations if any combination does not have exactly one observation.
If you have two or more fixed categorical factors, use Fit General Linear Model if you have all fixed factors or Fit Mixed Effects Model if you have random factors.
For more information on factors, go to Factors and factor levels and Fixed and random factors.
- The response variable should be continuous or ordinal
- If the response variable is categorical, your model is less likely to meet the assumptions of the analysis, to accurately describe your data, or to make useful predictions.
- If your response variable has two categories, such as pass and fail, use Fit Binary Logistic Model.
- If your response variable contains three or more categories that have a natural order, such as strongly disagree, disagree, neutral, agree, and strongly agree, use Ordinal Logistic Regression.
- If your response variable contains three or more categories that do not have a natural order, such as scratch, dent, and tear, use Nominal Logistic Regression.
- If your response variable counts occurrences, such as the number of defects, use Fit Poisson Model.
- The sample data do not need to be normally distributed
The distributions for all block-treatment combinations should have the same shape and spread, but they don't have to follow a normal distribution.
- The design should include at least 5 blocks or treatments
- The Friedman test uses the test statistic, S, to calculate the p-value. Under the null hypothesis, the chi-square distribution approximates the distribution of S. The approximation is reasonably accurate when either the number of blocks or the number of treatments in the randomized block design is greater than 5.
- Each observation should be independent from all other observations
If your observations are dependent, your results might not be valid. Consider the following points to determine whether your observations are independent:
- If an observation provides no information about the value of another observation, the observations are independent.
- If an observation provides information about another observation, the observations are dependent.
If you have a dependent observations, go to Analyzing a repeated measures design. For more information about samples, go to How are dependent and independent samples different?
- Collect data using best practices
To ensure that your results are valid, consider the following guidelines:
- Make certain that the data represent the population of interest.
- Collect enough data to provide the necessary precision.
- Measure variables as accurately and precisely as possible.
- Record the data in the order it is collected.