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Excluding response time (RT) outliers impacts statistical analysis. Simulation shows z-score methods introduce minimal bias, while no exclusion causes the most significant bias in RT distribution recovery.

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Area of Science:

  • Cognitive Psychology
  • Psychometric Methods
  • Statistical Modeling

Background:

  • Response time (RT) outlier exclusion is standard practice in RT research to enhance signal-to-noise ratio.
  • Multiple outlier exclusion methods exist, but their comparative effectiveness in recovering uncontaminated RT distributions is unclear.

Purpose of the Study:

  • To simulate and compare the performance of ten different outlier exclusion methods in recovering true response time distributions.
  • To evaluate the bias introduced by various outlier exclusion techniques.

Main Methods:

  • Simulated two response time distributions with predefined differences in each iteration.
  • Introduced outliers using two approaches: tail-based and overlapping distributions.
  • Applied ten distinct outlier exclusion methods and assessed the proportion of significant differences recovered.
  • Quantified bias as the deviation from significant differences in uncontaminated samples.

Main Results:

  • Significant differences in bias were observed across the ten outlier exclusion methods.
  • Certain methods exhibited a high rate of Type-I errors, rendering them unsuitable for use.
  • Exclusion methods based on z-scores/standard deviations demonstrated minimal bias.
  • Absence of any outlier exclusion resulted in the largest absolute bias.

Conclusions:

  • The choice of outlier exclusion method critically impacts the accuracy of response time distribution recovery.
  • Z-score based outlier exclusion methods offer a favorable balance between bias reduction and data integrity.
  • Researchers should carefully select outlier exclusion techniques to avoid inflating Type-I errors and distorting findings.