posthocAnalyse

Posthoc analysis, often referred to as post hoc testing, is a statistical procedure performed after a study or experiment has been conducted and its initial results analyzed. The primary purpose of posthoc analysis is to explore specific differences between groups or conditions when an overall statistical test, such as an ANOVA, has revealed a significant effect. These tests are used to determine which particular groups or treatment conditions are significantly different from each other, pinpointing the sources of the overall significance.

Without posthoc tests, a significant result from an omnibus test (like an ANOVA) only tells us that at least one group mean is different from another, but not where those differences lie. For instance, if an ANOVA compares the effectiveness of three different teaching methods, a significant result indicates that the methods are not all equally effective. A posthoc test would then identify which specific teaching methods led to significantly better or worse outcomes compared to the others.

Commonly used posthoc tests include Tukey's HSD (Honestly Significant Difference), Bonferroni correction, Scheffé's test, and Dunnett's test. The choice of posthoc test depends on factors such as the specific research question, the number of groups being compared, and the desired level of statistical power and conservatism. It is important to note that conducting multiple posthoc tests increases the risk of Type I errors (falsely rejecting the null hypothesis). Therefore, many posthoc tests incorporate adjustments to control the family-wise error rate.