The normal distribution is not normal in psychological data: Moving beyond parametric dogma.

Area of Science:

• Psychological Science
• Statistical Methods

Background:

• Parametric statistical methods, historically favored in psychology, often assume normal distribution.
• Psychological and mental health data frequently violate normality, showing skewness, kurtosis, and outliers.
• Common constructs like stress and anxiety often have non-normal distributions, rendering parametric tests inappropriate.

Purpose of the Study:

• To highlight common deviations from normality in psychological data.
• To advocate for a shift towards assumption-light analytical strategies in psychological research.
• To promote the use of flexible statistical methods for improved validity and reproducibility.

Main Methods:

• Illustration of common deviations from normality in psychological data.
• Discussion of non-parametric tests as alternatives.
• Exploration of resampling techniques like bootstrapping and Monte Carlo simulations.

Main Results:

• Violations of normality assumptions increase Type I and II errors and bias effect estimates.
• Non-parametric tests and resampling methods offer robust alternatives to parametric tests.
• Flexible methods provide greater accuracy without strict distributional assumptions.

Conclusions:

• A paradigm shift towards assumption-light statistical strategies is necessary in psychological science.
• Emphasizing data visualization, transparent reporting, and statistical education is crucial.
• Broader adoption of flexible methods will enhance the validity, interpretability, and reproducibility of psychological findings.