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Why and how we should join the shift from significance testing to estimation.

Daniel Berner1, Valentin Amrhein1

  • 1Department of Environmental Sciences, Zoology, University of Basel, Basel, Switzerland.

Journal of Evolutionary Biology
|May 18, 2022
PubMed
Summary

Null hypothesis significance testing is problematic due to p-value variability and biased effect size estimates. Evolutionary biology research should shift towards describing compatible hypotheses with confidence intervals instead of relying on significance testing.

Keywords:
compatibility intervaleffect sizenull hypothesisp-valuescientific methodstatistical inference

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Area of Science:

  • Ecology
  • Evolutionary Biology
  • Statistical Inference

Background:

  • Null hypothesis significance testing (NHST) is widely used but faces criticism.
  • P-values exhibit high study-to-study variability, questioning dichotomous inference.
  • NHST can lead to over- or underestimated effect sizes, influenced by statistical power.

Purpose of the Study:

  • To illustrate motivations for a paradigm shift away from NHST.
  • To assess the continued reliance on NHST in evolutionary biology literature.
  • To propose an alternative statistical approach for ecological and evolutionary studies.

Main Methods:

  • Simulations were used to demonstrate issues with NHST.
  • A content analysis screened 48 papers from the Journal of Evolutionary Biology (2020).
  • Screening focused on the use of significance testing, hypothesis specification, and reporting of results.

Main Results:

  • Significance testing remains prevalent in evolutionary biology, with studies testing default null hypotheses.
  • Many studies (63%) falsely claimed the absence of an effect based on non-significant results.
  • Pre-study power calculations and alternative hypotheses were rarely reported.

Conclusions:

  • Current studies in ecology and evolutionary biology are largely exploratory and descriptive.
  • A shift from statistical hypothesis testing to describing multiple, data-compatible hypotheses is recommended.
  • Compatibility (confidence) intervals offer a viable alternative for presenting findings.