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Related Concept Videos

Decision Making: P-value Method01:09

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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
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P-value is one of the most crucial concepts in statistics.
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Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
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How to Use and Report on p-values.

Christy K Boscardin1,2, Justin L Sewell1, Martin G Tolsgaard3

  • 1Department of Medicine, University of California, San Francisco, California, US.

Perspectives on Medical Education
|April 29, 2024
PubMed
Summary
This summary is machine-generated.

The p-value threshold for statistical significance is debated. This paper guides researchers to use p-values more effectively by reporting effect sizes and confidence intervals.

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Area of Science:

  • Statistics in quantitative research
  • Health professions education research

Background:

  • The p-value and its "P < 0.05" threshold are traditional measures of statistical significance.
  • Concerns exist regarding p-values misleading findings when practical significance and test suitability are ignored.

Purpose of the Study:

  • To provide guidance on the appropriate use of p-values in health professions education research.
  • To address the controversy surrounding p-value interpretation and reporting.

Main Methods:

  • Overview of the ongoing debate on p-value usage and definition.
  • Highlighting common pitfalls in p-value interpretation and application.
  • Outlining recommendations for effective statistical reporting.

Main Results:

  • The American Statistical Association and leading journals challenge the overreliance on p-values.
  • Common pitfalls include misinterpretation, overemphasis, and false dichotomization of results.
  • Recommendations include reporting effect sizes, confidence intervals, and conducting sensitivity analyses.

Conclusions:

  • Researchers should move towards a more nuanced interpretation of p-values.
  • Effective statistical reporting requires considering factors beyond the p-value threshold.
  • Adopting recommended practices enhances the informativeness of research findings.