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The power of effect size stabilization.

Benjamin Kowialiewski1,2

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Behavior Research Methods
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Summary
This summary is machine-generated.

Effect size stabilization helps achieve unbiased samples in psychological research but may increase the risk of missing true effects (Type 2 errors). It is best used for parameter estimation, not hypothesis testing.

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Area of Science:

  • Psychological research methodology
  • Statistical inference

Background:

  • Determining adequate sample size is crucial in psychological experiments to balance detecting true effects and avoiding false positives.
  • Effect size stabilization, a form of optional stopping, has been proposed to define sample size without increasing false positive risks.

Purpose of the Study:

  • To investigate the properties of effect size stabilization in psychological research.
  • To understand the implications of using effect size stabilization for sample size determination and hypothesis testing.

Main Methods:

  • Simulations were conducted to examine effect size stabilization properties.
  • Parametric modulation of the true population effect size and the strictness of the stabilization rule were incorporated into the simulations.

Main Results:

  • Effect size stabilization, as a form of optional stopping, consistently yields unbiased samples in the long run.
  • However, effect size stabilization does not ensure the detection of a true population effect, increasing the probability of Type 2 errors.

Conclusions:

  • Effect size stabilization is not recommended for hypothesis testing due to an increased risk of Type 2 errors.
  • Researchers should utilize effect size stabilization procedures primarily for achieving accurate parameter estimates rather than for making definitive statistical tests.