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Effect size - one size doesn't fit all.

Gary Cutter1

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This summary is machine-generated.

This study cautions against solely avoiding p-values, highlighting the importance of effect sizes and contextual interpretation in biostatistics and medical research for a comprehensive understanding of study findings.

Keywords:
effect sizep-valuesreporting standards

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Area of Science:

  • Biostatistics
  • Medical Research
  • Statistical Analysis

Background:

  • The exclusion of p-values in statistical analysis is a growing trend, but this binary approach may introduce new challenges.
  • Over-reliance on or complete avoidance of specific statistical measures can lead to misinterpretation of research findings.

Purpose of the Study:

  • To elaborate on the appropriate use of p-values and effect sizes in research.
  • To emphasize the critical importance of contextual interpretation in biostatistics and medical research.
  • To caution against the over- or under-interpretation of various statistical measures.

Main Methods:

  • Literature review and conceptual analysis of statistical practices.
  • Discussion of the utility of p-values and effect sizes.
  • Emphasis on the integration of biological and statistical principles.

Main Results:

  • No single statistical measure is sufficient for evaluating a study's value.
  • Contextual understanding is paramount for interpreting statistical results.
  • A balanced approach considering multiple measures is necessary.

Conclusions:

  • Researchers must integrate biostatistical and biological/medical context for accurate interpretation.
  • Avoidance of p-values alone is not a solution; a nuanced approach is required.
  • Effective research evaluation necessitates a holistic view of statistical outputs and their real-world implications.