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Multiple correlations and Bonferroni's correction

F Curtin1, P Schulz

  • 1Department of Psychiatry, Geneva University, Switzerland.

Biological Psychiatry
|November 3, 1998
PubMed
Summary
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Adjusting statistical significance for multiple correlations in psychiatric research is crucial to avoid false positives. Bonferroni

Area of Science:

  • Psychiatric research
  • Biostatistics
  • Statistical significance

Background:

  • Psychiatric research frequently involves calculating correlation coefficients between biological measurements and clinical scales.
  • Performing numerous correlation analyses heightens the risk of Type I errors, leading to false conclusions of significant associations.

Purpose of the Study:

  • To adjust statistical significance thresholds for correlation coefficients when multiple comparisons are made.
  • To evaluate the impact of Bonferroni's correction on significance levels across varying numbers of correlation coefficients (k = 1, 5, 10, 20, 50, 100).

Main Methods:

  • Bonferroni's correction was applied to adjust significance thresholds for sets of correlation coefficients.
  • The study analyzed how adjusted significance levels change with increasing numbers of correlations and varying sample sizes.

Related Experiment Videos

Main Results:

  • The adjustment of significance thresholds has a more pronounced effect when the number of correlations increases from 1 to 5 compared to increases from 50 to 100.
  • A correlation coefficient that is significant at the 5% level when analyzed in isolation may fall below the adjusted significance threshold when part of a larger set.

Conclusions:

  • Adjusting for multiple comparisons is essential in psychiatric research to maintain statistical rigor.
  • Researchers are advised to focus on the most relevant variables or employ multivariate statistical methods to mitigate the risk of Type I errors.