Multiple Hypothesis Testing Framework
Jump to navigation
Jump to search
A Multiple Hypothesis Testing Framework is a statistical hypothesis testing framework that addresses the challenges of conducting multiple statistical hypothesis tests simultaneously while controlling overall error rates.
- AKA: Multiple Comparisons Framework, Multiple Testing Framework, Simultaneous Hypothesis Testing Framework.
- Context:
- It can typically control Family-Wise Error Rate (FWER) to limit the probability of any Type I Error.
- It can typically control False Discovery Rate (FDR) to limit the expected proportion of false positives.
- It can often employ Bonferroni Correction for conservative error control.
- It can often utilize Benjamini-Hochberg Procedure for less conservative FDR control.
- It can range from being a Conservative Multiple Hypothesis Testing Framework to being a Liberal Multiple Hypothesis Testing Framework, depending on its error control stringency.
- It can range from being a Single-Step Multiple Hypothesis Testing Framework to being a Sequential Multiple Hypothesis Testing Framework, depending on its adjustment procedure.
- It can range from being a Independent Multiple Hypothesis Testing Framework to being a Dependent Multiple Hypothesis Testing Framework, depending on its test correlation structure.
- It can range from being a Parametric Multiple Hypothesis Testing Framework to being a Non-Parametric Multiple Hypothesis Testing Framework, depending on its distributional assumptions.
- ...
- Example(s):
- Genomic Association Testing Frameworks, such as:
- Genome-Wide Association Study Framework testing millions of SNPs.
- Gene Expression Analysis Framework comparing thousands of genes.
- Clinical Trial Multiplicity Frameworks, such as:
- Multiple Endpoint Testing Framework for primary and secondary outcomes.
- Subgroup Analysis Framework for treatment effect heterogeneity.
- Neuroimaging Analysis Frameworks, such as:
- Voxel-Wise Testing Framework for brain imaging data.
- Network Analysis Framework for connectivity studies.
- ...
- Genomic Association Testing Frameworks, such as:
- Counter-Example(s):
- A Single Hypothesis Testing Framework, which tests only one hypothesis.
- A Exploratory Data Analysis Framework, which doesn't require error control.
- A Descriptive Statistics Framework, which doesn't involve hypothesis testing.
- See: Statistical Hypothesis Testing Task, Multiple Testing Problem, Family-Wise Error Rate, False Discovery Rate, Bonferroni Correction, Holm-Bonferroni Method, Benjamini-Hochberg Procedure, Statistical Decision Framework, Type I Error, Statistical Power.