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Fragility Part I: a guide to understanding statistical power.

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This paper clarifies statistical power, explaining how p-values, effect size, sample size, and variance impact research reproducibility. Understanding a priori power analysis is crucial for robust scientific findings.

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Area of Science:

  • Statistics
  • Research Methodology
  • Biostatistics

Background:

  • Addresses the critical knowledge-to-practice gap in statistical power.
  • Highlights the impact of p-value, effect size, sample size, and variance on statistical power.

Discussion:

  • Compares a priori and post hoc power analyses, emphasizing the essential role of a priori analysis.
  • Discusses the implications for research reproducibility and statistical fragility.

Key Insights:

  • Statistical power is influenced by four key factors: p-value, effect size, sample size, and variance.
  • A priori power analysis is essential for ensuring research reproducibility.
  • Understanding statistical power enhances the reliability of scientific research.

Outlook:

  • Empowers physician-scientists with enhanced statistical tools and contextual understanding.
  • Aims to improve the rigor and reproducibility of scientific research.
  • Provides a foundation for navigating statistical complexities in scientific studies.