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Why checking model assumptions using null hypothesis significance tests does not suffice: A plea for plausibility.

Jesper Tijmstra1

  • 1Department of Methodology and Statistics, Faculty of Social Sciences, Tilburg University, PO Box 90153, 5000 LE, Tilburg, The Netherlands. j.tijmstra@uvt.nl.

Psychonomic Bulletin & Review
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Summary

Null hypothesis significance testing (NHST) alone is insufficient for evaluating statistical model assumptions. Assessing prior plausibility is crucial for determining model validity and reliable inferences.

Keywords:
Bayesian statisticsBelief updatingStatistical inferenceStatistics

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Area of Science:

  • Statistics
  • Statistical Modeling

Background:

  • Statistical model assumptions are critical for valid inferences.
  • Null hypothesis significance testing (NHST) is commonly used but may not fully assess these assumptions.

Purpose of the Study:

  • To investigate the adequacy of NHST for evaluating statistical model assumptions.
  • To highlight the importance of prior plausibility in assumption assessment.

Main Methods:

  • Conceptual analysis of NHST within the framework of statistical model evaluation.
  • Discussion on the role of prior plausibility in Bayesian and frequentist approaches.

Main Results:

  • NHST-based tests offer limited confirmation of null hypotheses regarding model assumptions.
  • These tests do not quantify the data's support for the null hypothesis or its post-data plausibility.
  • Ignoring prior plausibility leads to uncertainty about model adequacy and validity.

Conclusions:

  • NHST is insufficient for a comprehensive evaluation of statistical model assumptions.
  • Incorporating prior plausibility is essential for reliable statistical modeling and inference.
  • Determining the plausibility of model assumptions requires considering evidence beyond simple significance testing.