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Adjustment of p values for multiple hypotheses: why, when and how
1Regional Centre for Child and Youth Mental Health and Child Welfare, Norwegian University of Science and Technology, Trondheim, Norway stian.lydersen@ntnu.no.
Investigating multiple hypotheses increases the risk of type I errors. This article details multiplicity adjustment methods to control these statistical errors and provides recommendations for researchers.
Area of Science:
- Statistics
- Biostatistics
- Research Methodology
Background:
- Single studies often involve multiple hypotheses, such as examining various outcomes, time points, or subgroups.
- This practice elevates the likelihood of encountering false positive results, known as type I errors.
Purpose of the Study:
- To describe common methods for multiplicity adjustment in statistical analysis.
- To provide recommendations for controlling type I error rates in studies with multiple hypotheses.
Main Methods:
- The article reviews established statistical procedures designed for multiplicity adjustment.
- Key methods for controlling the probability of type I errors are discussed.
Main Results:
- Multiple statistical methods exist to manage the increased risk of type I errors when testing multiple hypotheses.
- The article outlines these techniques and offers guidance on their application.
Conclusions:
- Appropriate multiplicity adjustment is crucial for maintaining the integrity of research findings.
- Implementing recommended adjustment methods helps ensure reliable statistical conclusions in complex studies.
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