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 Express cannot complete the calculations if any combination does not have exactly one observation.
For more information on factors, go to Factors and factor levels.
- 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 you have a categorical response variable, use Cross Tabulation and Chi-Square.
- 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.
- Collect data using best practices
To ensure that your results are valid, consider the following guidelines:
- Make sure 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.