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Related Experiment Videos

Making 'null effects' informative: statistical techniques and inferential frameworks.

Christopher Harms1,2, Daniël Lakens2

  • 1Department of Psychology, University of Bonn, Germany.

Journal of Clinical and Translational Research
|March 16, 2019
PubMed
Summary
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Interpreting null effects in research requires moving beyond traditional significance tests. New statistical methods like equivalence tests and Bayesian analysis offer more informative conclusions about the absence of an effect.

Area of Science:

  • Statistical inference
  • Scientific methodology
  • Research integrity

Background:

  • Cumulative scientific knowledge generation relies on accurate interpretation of research findings, including null effects.
  • Traditional null-hypothesis significance testing (NHST) is often misinterpreted as evidence for the absence of an effect when results are non-significant.
  • This misinterpretation hinders accurate conclusions and impedes scientific progress, particularly in areas like clinical trials.

Purpose of the Study:

  • To provide researchers with methods to statistically evaluate null-results beyond traditional NHST.
  • To explain and demonstrate the application of equivalence tests, Bayesian estimation, and Bayes factors for interpreting null effects.
  • To foster more informative conclusions from studies reporting null findings.
Keywords:
bayes factorsbayesian estimationequivalence testinghypothesis

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Main Methods:

  • Explanation of statistical approaches for evaluating null effects: equivalence tests, Bayesian estimation, and Bayes factors.
  • A worked example illustrating the application and interpretation of these statistical tools.
  • Discussion of the philosophical underpinnings and limitations of statistical approaches to null-effects.

Main Results:

  • Equivalence tests, Bayesian estimation, and Bayes factors provide statistically sound methods for interpreting null effects.
  • These methods allow researchers to move beyond the limitations of NHST and draw more nuanced conclusions.
  • No statistical approach can definitively 'prove' a null hypothesis; rather, they offer evidence regarding the plausibility of effects.

Conclusions:

  • Researchers should adopt advanced statistical methods like equivalence tests and Bayesian approaches to interpret null effects accurately.
  • Improved statistical inference leads to more accurate study interpretations and facilitates robust knowledge generation.
  • The availability of user-friendly software makes these methods accessible for complementing or replacing traditional NHST.