Missingness, Multiplicity, and Degrees of Freedom
Use this section when missing data, multiple comparisons, or repeated analytic choices could change how strongly a result should be interpreted.
When to use this category
Use this category when your question is about whether missing data, multiple comparisons, or repeated analytic choices change how strongly a result can be interpreted.
This section covers missing-value handling, analysis-family multiplicity, and researcher degrees of freedom; it is not the place for study design and independence questions or for model-diagnostics questions.
If your main question is whether observations are independent, whether batches or repeated measures change the design, or whether a regression model is mis-specified, go to Design and Independence or Model Diagnostics instead.
Quick routing
| If your main question is... | Go to... | Why |
|---|---|---|
| Are missing values, imputation, or dropped rows changing the analysis N or what must be disclosed? | Missing Data and Attrition | Covers missingness detection, imputation options, attrition, and MCAR / MAR / MNAR assumptions |
| Are there multiple pairwise comparisons in one figure, or several claim-bearing figures addressing the same question? | Multiplicity and Analysis Families | Covers within-figure multiplicity, analysis families, and family-wide policies such as Holm correction |
| Are outlier exclusions, subgroup choices, or retesting steps making the analysis more flexible after seeing the data? | Outliers and Researcher Degrees of Freedom | Covers outlier removal, exploratory branching, alpha-inflation heuristics, and disclosure of repeated analytic choices |
Child pages
- Missing Data and Attrition
- Multiplicity and Analysis Families
- Outliers and Researcher Degrees of Freedom
Start here
- Start with Missing Data and Attrition if the main issue is missing values, imputation, dropped rows, or attrition disclosure.
- Start with Multiplicity and Analysis Families if the main issue is Tukey HSD, Holm, Bonferroni, pairwise comparisons, or several related claim-bearing figures.
- Start with Outliers and Researcher Degrees of Freedom if the main issue is outlier exclusion, subgroup selection, retesting, or p-hacking risk.
Related
- Related links are registered in frontmatter and rendered below this stub.