Data considerations for 1-Sample Equivalence Test

To ensure that your results are valid, consider the following guidelines when you collect data, perform the analysis, and interpret the results.

The sample data should not be severely skewed, and the sample size should be greater than 20: If your sample size is greater than 20 and the underlying distribution is unimodal and continuous, the equivalence test performs appropriately even if the data are mildly skewed. If your sample size is less than 20, you should graph the data to check for skewness and unusual observations. If the data are severely skewed or have many unusual observations, use caution when you interpret the results.
The sample data should be selected randomly: In statistics, random samples are used to make generalizations, or inferences, about a population. If your data were not collected randomly, your results may not represent the population. For more information, go to Randomness in samples of data.
Each observation should be independent from all other observations: If you have paired (dependent) observations on the same person or item, use Equivalence Test with Paired Data. For more information, go to How are dependent and independent samples different?.
Determine an appropriate sample size: Your sample should be large enough that the test has sufficient power to demonstrate equivalence when it is true. If the equivalence test has low power, you may mistakenly conclude that the difference is not within your equivalence limits when it actually is. To determine the appropriate sample size for your equivalence test, go to Power and Sample Size for 1-Sample Equivalence Test.