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Second-generation p-values: Improved rigor, reproducibility, & transparency in statistical analyses.

Jeffrey D Blume1, Lucy D'Agostino McGowan2, William D Dupont3

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|March 23, 2018
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Summary
This summary is machine-generated.

Introducing second-generation p-values (pδ), a novel statistical tool that ensures scientific relevance alongside statistical significance. This method enhances Type I error control and improves reproducibility in data-rich research environments.

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Area of Science:

  • Statistical methodology
  • Scientific research integrity

Background:

  • Traditional p-values assess statistical significance but not scientific relevance.
  • Controlling Type I errors is crucial for reliable scientific findings.
  • Data-rich environments often face challenges with traditional statistical adjustments.

Purpose of the Study:

  • To introduce a novel extension of the p-value, termed the second-generation p-value (pδ).
  • To formally integrate scientific relevance into statistical hypothesis testing.
  • To enhance Type I error control and improve reproducibility in scientific research.

Main Methods:

  • Developed a second-generation p-value (pδ) based on a pre-specified interval null hypothesis.
  • Defined pδ as the proportion of data-supported hypotheses that are also null hypotheses.
  • Utilized pδ to indicate compatibility with null, alternative, or inconclusive hypotheses.

Main Results:

  • Second-generation p-values (pδ) quantify scientific relevance alongside statistical significance.
  • pδ provides natural Type I error control and a proper adjustment for multiple comparisons.
  • The method reduces false discovery rates, especially in data-rich settings.

Conclusions:

  • Second-generation p-values (pδ) offer a more reliable statistical summary than traditional p-values.
  • This approach promotes transparency, rigor, and reproducibility in scientific results.
  • pδ a priori specifies practically meaningful hypotheses, advancing statistical practice.