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[Should statistical significance be retired?]

A Richter1, A Zink2

  • 1Institut für Community Medicine, Universitätsmedizin Greifswald, Walther-Rathenau-Str. 48, 17475, Greifswald, Deutschland. adrian.richter@uni-greifswald.de.

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PubMed
Summary
This summary is machine-generated.

Researchers debate abolishing statistical significance, a concept often misused for binary decisions. While agreeing with criticism, the authors suggest a pragmatic approach with guidelines instead of outright abolition due to lack of alternatives.

Keywords:
Clinical relevanceConfidence intervalsHypothesesStatistical testsp-value

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Area of Science:

  • Statistics
  • Scientific Methodology
  • Research Ethics

Background:

  • A recent call to abolish statistical significance in Nature sparked widespread debate.
  • The concept is frequently misapplied for dichotomous (yes/no) research decisions.
  • Criticism of statistical significance's application is a long-standing issue in scientific discourse.

Purpose of the Study:

  • To critically evaluate the call for abolishing statistical significance.
  • To review the historical and current applications of statistical significance.
  • To explore potential alternatives and propose a pragmatic way forward.

Main Methods:

  • Literature review of criticisms and applications of statistical significance.
  • Analysis of the arguments presented by Amrhein et al.
  • Comparative assessment of statistical significance and alternative methods.

Main Results:

  • The authors concur with the critique of statistical significance's misuse.
  • They find the demand for abolition exaggerated due to the absence of universally agreed alternatives.
  • The study highlights the diverse applications of statistical significance, often overlooked in abolitionist arguments.

Conclusions:

  • A pragmatic approach, including targeted instructions for researchers and reviewers, is recommended.
  • Outright abolition of statistical significance is deemed premature.
  • Further discussion and development of alternative statistical practices are necessary.