In many study designs, data evaluation involves comparison of results for which there is an expectation of differences between data sets. In these cases, evaluations include use of statistical tests such as Student’s t test and analysis of variance. For data evaluations, if the hypothesis is that the data sets are equivalent, equivalence testing techniques may be applicable. In equivalence testing, the null hypothesis is established such that the statistical test is for similarily. Typical applications of equivalence testing include process scale-up, control charting, methods transfer between laboratories, and product design and maintenance. In order to support studies for which the results were hypothesized to be equivalent, statistical and mathematical techniques were compared and contrasted using a range of exemplary data sets. These techniques included methods such as repeatability & reproducibility, critical difference, confidence interval analysis, and two one-sided t-test (TOST). The results of these comparisons along with advantages and disadvantages of the techniques will be discussed.