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Related Experiment Videos

Sample size determination for a t test given a t value from a previous study: A FORTRAN 77 program.

R Gillett1

  • 1School of Psychology, University of Leicester, Leicester LE1 7RH, U.K. rtg@le.ac.uk

Behavior Research Methods, Instruments, & Computers : a Journal of the Psychonomic Society, Inc
|January 31, 2002
PubMed
Summary
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Researchers often miscalculate sample sizes due to uncertain effect size estimates. This study introduces a new method using prior study data to determine optimal sample sizes, improving statistical power for various t-tests.

Area of Science:

  • Biostatistics
  • Statistical Methods
  • Research Design

Background:

  • Traditional sample size formulas struggle with uncertainty in effect size estimates.
  • Substituting point estimates from prior studies into standard formulas can lead to significant power loss.
  • Accurate sample size determination is crucial for reliable research findings.

Purpose of the Study:

  • To present a novel method for sample size determination that accounts for uncertainty in effect size estimates.
  • To improve the statistical power of studies when effect sizes are not precisely known.
  • To provide a practical tool for researchers conducting various types of t-tests.

Main Methods:

  • Utilizes the t-value and sample size from a previous study (pilot or related) to create a distribution of probable effect sizes.

Related Experiment Videos

  • Calculates the required sample size for a new study to achieve desired expected power across this distribution.
  • A FORTRAN 77 program is developed for efficient calculation across multiple t-test variations.

    • Main Results:

    • The proposed method effectively handles uncertainty in effect size estimation.
    • It ensures a desired level of statistical power is achieved on average over plausible effect sizes.
    • The FORTRAN 77 program facilitates rapid sample size calculations for independent and related t-tests, correlation coefficients, and multiple regression coefficients.

    Conclusions:

    • This new approach offers a more robust solution for sample size determination in the face of effect size uncertainty.
    • Researchers can achieve more reliable statistical power by employing this method.
    • The accompanying program provides a valuable resource for practical application in diverse research settings.