PosthocAnalysen

PosthocAnalysen are statistical analyses conducted after data collection to explore patterns or relationships not specified in the original study hypothesis. They are often described as exploratory analyses and can help generate new hypotheses or explain unexpected findings.

Because they test many hypotheses on the same data set, post hoc analyses increase the risk of type I errors. Without proper adjustments, a reported significant finding may be due to chance. Researchers may apply corrections for multiple testing or emphasize the exploratory nature, demanding replication. Related concerns include p-hacking and data dredging, which highlight the need for careful interpretation and transparency.

Common forms include subgroup analyses, post hoc comparisons after ANOVA or regression, or searching for interactions or outliers. They should be clearly labeled as exploratory and, when possible, split data into discovery and validation samples to assess robustness.

Best practices emphasize using post hoc analyses to generate rather than confirm hypotheses: predefine hypotheses when possible; document all analyses conducted; adjust p-values; report effect sizes and confidence intervals; be cautious about overinterpretation; and seek replication in independent samples.

Relation to broader research practice: post hoc analyses are distinct from preregistered confirmatory analyses and should be used to inform future studies rather than draw strong conclusions by themselves. They are a normal part of data analysis but must be interpreted with skepticism and transparency, clearly distinguishing exploratory findings from confirmatory evidence.