Statistical assumptions of substantive analyses across the general linear model: a mini-review

Area of Science:

• Statistics
• Research Methodology

Background:

• Statistical inference validity hinges on data meeting test assumptions.
• Literature often lacks reporting of these assumptions, hindering reproducibility.
• Researchers may lack familiarity with assumption checking and remedies.

Purpose of the Study:

• To review key statistical assumptions for general linear model tests.
• To outline methods for checking these assumptions.
• To identify remedies for assumption violations and associated problems.

Main Methods:

• Literature review of statistical assumptions within the general linear model framework.
• Compilation of techniques for assessing assumption adherence.
• Identification of common remedies and potential issues arising from unmet assumptions.

Main Results:

• Key assumptions for common statistical tests are detailed.
• Practical methods for assumption verification are presented.
• Guidance on addressing assumption violations is provided.

Conclusions:

• Understanding and verifying statistical assumptions is crucial for valid research.
• This review serves as a practical guide for researchers using the general linear model.
• Promoting attention to assumptions enhances the reliability of statistical findings.