Post-hoc analysis

Definition

Post-hoc analysis refers to additional analyses performed after the initial statistical tests have been conducted, often to explore specific differences or relationships within the data that were not specified a priori. This process helps researchers understand patterns that emerge from their data, particularly in complex designs, and aids in interpreting the results more thoroughly, especially when significant differences are found.

5 Must Know Facts For Your Next Test

1. Post-hoc analyses are crucial when initial tests show significant results, as they can identify which specific groups or conditions are different.
2. These analyses should be performed with caution due to the increased risk of Type I errors from conducting multiple comparisons without proper adjustments.
3. Common post-hoc tests include Tukey's HSD, Scheffé's test, and Dunn's test, each designed to handle different types of data and research questions.
4. The choice of post-hoc test often depends on the assumptions of the data, such as normality and homogeneity of variance.
5. Documenting post-hoc analyses is essential for transparency in research, allowing others to understand how conclusions were drawn from the data.

Review Questions

• How does post-hoc analysis enhance the interpretation of results obtained from initial statistical tests?
  • Post-hoc analysis allows researchers to delve deeper into their data after finding significant results from initial tests. By identifying specific groups or conditions that differ from one another, post-hoc tests provide clearer insights into the patterns within the data. This process is essential for understanding the nuances behind significant findings and for making informed conclusions based on the research.
• What are some common pitfalls associated with post-hoc analyses, particularly in relation to Type I errors?
  • One major pitfall of post-hoc analyses is the increased risk of Type I errors due to multiple comparisons being conducted without appropriate corrections. When researchers perform numerous tests, they may mistakenly conclude that there are significant effects simply due to chance. To mitigate this risk, adjustments such as the Bonferroni correction should be utilized to maintain an acceptable level of significance across all tests performed.
• Evaluate how the selection of a specific post-hoc test can impact the conclusions drawn from data analysis.
  • The selection of a post-hoc test can significantly influence the findings and interpretations derived from data analysis. Different tests have various assumptions regarding the data, such as normality or equal variances, and choosing an inappropriate test can lead to misleading conclusions. For example, using Tukey's HSD when variances are unequal might yield invalid results. Thus, it's vital for researchers to understand their data characteristics and choose suitable post-hoc methods to ensure robust and reliable interpretations.

Related terms

Type I Error: The incorrect conclusion that a significant effect exists when it actually does not, often associated with multiple comparisons in post-hoc analyses.

Multiple Comparisons: The situation where multiple statistical tests are performed simultaneously, increasing the risk of Type I errors without proper adjustment methods.

Bonferroni Correction: A statistical method used to adjust significance levels when conducting multiple comparisons, aimed at reducing the likelihood of Type I errors.