Multiple hypothesis tests based On conditional differences in means

Sascha Wörz, Heinz Bernhardt, Anja Gräff, Huber Stefan

Research output: Contribution to journalArticlepeer-review


Many hypothesis tests are univariate tests and cannot cope with multiple hypothesis without an auxiliary procedure as e. g. the Bonferroni-Holm-procedure. At the same time, there is an urgent need for testing multiple hypothesis due to the very simple existing methods as the Bonferroni-correction or the Bonferroni-Holm-procedure, which suffers from a very small local significance level to detect statistical inferences or the drawback that logical and statistical dependencies among the test statistics are not used, whereby its detection is NP-hard. In honour of this occasion, we present a multiple hypothesis test for i.i.d. random variables based on conditional differences in means, which is capable to cope with multiple hypothesis and does not suffer on such drawbacks as the Bonferroni-correction or the Bonferroni-Holm-procedure. Thereby, the computation time can be neglected.

Original languageEnglish
Pages (from-to)1033-1041
Number of pages9
JournalCommunications in Statistics - Theory and Methods
Issue number4
StatePublished - 16 Feb 2019


  • Conditional Differences In Means
  • Multiple Hypothesis Testing
  • Statistical Inferences in Multivariate Data


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