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Statistical software R and SAS may produce incorrect p-values in analyses of variance (ANOVA) and mixed-effects models. Users must verify software options for accurate results, as traditional ANOVA may be more reliable than modern mixed-effects model functions.

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Area of Science:

  • Statistics
  • Computational Statistics

Background:

  • Common statistical belief: R's two-way analysis of variance (ANOVA) yields correct p-values.
  • Assumption: SAS and R mixed-effects models for balanced experiments provide accurate p-values.

Purpose of the Study:

  • Compare the correctness of p-values generated by SAS and R.
  • Evaluate the reliability of statistical software for analyzing small experiments.

Main Methods:

  • Simulation study to assess Type I error rates.
  • Comparison of results from traditional ANOVA and mixed-effects models in SAS and R.

Main Results:

  • R's two-way ANOVA p-values can vary significantly based on selected options.
  • Type I error rates in SAS and R mixed-effects models deviate from nominal values.
  • Traditional ANOVA methods may be more reliable than some modern mixed-effects model procedures.

Conclusions:

  • Statistical software choices and options critically impact p-value accuracy.
  • Users need to be aware of specific software settings for valid statistical inference.
  • Modern mixed-effects model functions require careful validation against traditional methods.