List of terms

    Statistical Assumptions

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    Definition

    Analytical approaches and models assume certain characteristics of one’s data (e.g., statistical independence, random samples, normality, equal variance,…). Before running an analysis, these assumptions should be checked since their violation can change the results and conclusion of a study. Good practice in open and reproducible science is to report assumption testing in terms of the assumptions verified and the results of such checks or corrections applied.

    Related terms: Null Hypothesis Significance Testing (NHST), Statistical Significance, Statistical Validity, Transparency, Type I error, Type II error, Type M error, Type S error

    References:

    • Garson, G. D. (2012). Testing Statistical Assumptions (2012th ed.). North Carolina State University.
    • Hahn, G. J., & Meeker, W. Q. (1993). Assumptions for Statistical Inference. The American Statistician, 47(1), 1–11. https://doi.org/10.1080/00031305.1993.10475924
    • Hoekstra, R., Kiers, H., & Johnson, A. (2012). Are assumptions of well-known statistical techniques checked, and why (not)? Frontiers in Psychology, 3(137), 1–9. https://doi.org/10.3389/fpsyg.2012.00137
    • Nimon, K. F. (2012). Statistical Assumptions of Substantive Analyses Across the General Linear Model: A Mini-Review. Frontiers in Psychology, 3, 322. https://doi.org/10.3389/fpsyg.2012.00322

    Originally drafted by: Graham Reid

    Reviewed by: Jamie P. Cockcroft, Sam Parsons, Martin Vasilev, Julia Wolska

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