What is the proper way to apply the multiple comparison test?

Sangseok Lee, Dong Kyu Lee

Research output: Contribution to journalArticlepeer-review

102 Citations (Scopus)


Multiple comparisons tests (MCTs) are performed several times on the mean of experimental conditions. When the null hypothesis is rejected in a validation, MCTs are performed when certain experimental conditions have a statistically significant mean difference or there is a specific aspect between the group means. A problem occurs if the error rate increases while multiple hypothesis tests are performed simultaneously. Consequently, in an MCT, it is necessary to control the error rate to an appropriate level. In this paper, we discuss how to test multiple hypotheses simultaneously while limiting type I error rate, which is caused by α inflation. To choose the appropriate test, we must maintain the balance between statistical power and type I error rate. If the test is too conservative, a type I error is not likely to occur. However, concurrently, the test may have insufficient power resulted in increased probability of type II error occurrence. Most researchers may hope to find the best way of adjusting the type I error rate to discriminate the real differences between observed data without wasting too much statistical power. It is expected that this paper will help researchers understand the differences between MCTs and apply them appropriately.

Original languageEnglish
Pages (from-to)353-360
Number of pages8
JournalKorean journal of anesthesiology
Issue number5
Publication statusPublished - 2018 Oct


  • Alpha inflation
  • Analysis of variance
  • Bonferroni
  • Dunnett
  • Multiple comparison
  • Scheffé
  • Statistics
  • Tukey
  • Type I error
  • Type II error

ASJC Scopus subject areas

  • Anesthesiology and Pain Medicine

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