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This paper clarifies common p-value misconceptions in null hypothesis significance testing (NHST) for clinical researchers. Understanding p-values, confidence intervals, and effect sizes ensures accurate interpretation of epidemiological study results.

Keywords:
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Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Clinical research relies heavily on statistical analysis, particularly epidemiological study designs.
  • Misconceptions regarding p-values can lead to misinterpretation of research findings.

Purpose of the Study:

  • To address common misconceptions surrounding p-values in null hypothesis significance testing (NHST).
  • To guide researchers and readers in properly presenting and understanding NHST results.

Main Methods:

  • Discussion of p-value categorization (e.g., "significant at p <0.05," "not significant").
  • Emphasis on presenting and interpreting 95% confidence intervals (CI) alongside p-values.
  • Inclusion of effect size (ES) as a measure of effect magnitude.
  • Consideration of descriptive statistics and study power.

Main Results:

  • P-values are often oversimplified into categories, potentially obscuring nuanced findings.
  • Confidence intervals provide crucial context for clinical validity, irrespective of p-value significance.
  • Effect size should be interpreted cautiously to avoid overestimation.
  • Study power influences the accuracy of NHST, but low power does not render results entirely useless.

Conclusions:

  • Accurate interpretation of clinical research requires a comprehensive understanding of statistical concepts beyond simple p-value thresholds.
  • Proper reporting and interpretation of p-values, CIs, and ES enhance the reliability of epidemiological findings.
  • Acknowledging study limitations, including statistical power, is essential for valid scientific conclusions.