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The significance fallacy in inferential statistics.

Anton Kühberger1, Astrid Fritz2, Eva Lermer3

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Students often misunderstand statistical significance, equating it with large effect sizes rather than large sample sizes. This intuitive understanding persists even after statistical training, suggesting a focus on perceived effect magnitude over methodological rigor.

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Area of Science:

  • Psychology
  • Statistics
  • Empirical Science

Background:

  • Statistical significance is a key concept in empirical research.
  • The interpretation of statistical significance varies widely among researchers.
  • This study investigates the intuitive understanding of statistical significance.

Purpose of the Study:

  • To explore how students of psychology intuitively understand statistical significance.
  • To determine if students associate significance with effect size or sample size.

Main Methods:

  • Two psychological studies with labeled results ('significant' vs. 'non-significant') were presented to psychology students.
  • Participants estimated the effect sizes and sample sizes of the original studies.

Main Results:

  • Studies labeled as 'significant' were associated with estimations of large effect sizes.
  • Significance was largely unrelated to estimated sample size.
  • Non-significant results were perceived as having near-zero effect sizes.

Conclusions:

  • Psychology students, even after statistical training, predominantly equate statistical significance with medium to large effect sizes.
  • This suggests a common assumption that significance stems from genuine effects rather than methodological factors like increased sample size.
  • The findings highlight a potential misconception in the interpretation of statistical significance in empirical science.