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Researchers augmenting datasets with more data to achieve significance increases Type I error rates. A new statistic, paugmented, quantifies this inflation, promoting ethical research practices through transparent reporting.

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Area of Science:

  • Statistics
  • Research Methodology

Background:

  • Researchers often collect additional data when initial analyses yield encouraging but nonsignificant results.
  • This practice, known as dataset augmentation, can increase the risk of Type I errors (false positives).

Purpose of the Study:

  • To quantify the extent of Type I error inflation caused by dataset augmentation.
  • To review existing methods for controlling Type I errors during augmentation.
  • To introduce a new statistic for assessing Type I error inflation in post-hoc augmentation.

Main Methods:

  • Estimated Type I error inflation based on initial sample size, augmentation size, critical value, and maximum initial p-value.
  • Reviewed a priori methods for critical value adjustment.
  • Developed the 'paugmented' statistic for post-hoc augmentation.

Main Results:

  • One round of dataset augmentation can inflate Type I error rates, with maximum inflation reaching .0975.
  • Typical Type I error inflation values range from .0564 to .0883.
  • Existing a priori adjustment methods require pre-study planning.

Conclusions:

  • Post-hoc dataset augmentation significantly inflates Type I error rates.
  • The proposed 'paugmented' statistic quantifies this inflation.
  • Disclosing 'paugmented' can transition dataset augmentation from a questionable to an ethical research practice.