Small sample sizes: A big data problem in high-dimensional data analysis

Frank Konietschke* (Corresponding Author), Karima Schwab, Markus Pauly

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)
4 Downloads (Pure)


In many experiments and especially in translational and preclinical research, sample sizes are (very) small. In addition, data designs are often high dimensional, i.e. more dependent than independent replications of the trial are observed. The present paper discusses the applicability of max t-test-type statistics (multiple contrast tests) in high-dimensional designs (repeated measures or multivariate) with small sample sizes. A randomization-based approach is developed to approximate the distribution of the maximum statistic. Extensive simulation studies confirm that the new method is particularly suitable for analyzing data sets with small sample sizes. A real data set illustrates the application of the methods. </jats:p>
Original languageEnglish
Pages (from-to)687–701
Number of pages15
JournalStatistical Methods in Medical Research
Issue number3
Early online date24 Nov 2020
Publication statusPublished - 1 Mar 2021


  • Multiple contrast tests
  • max t-test
  • repeated measures
  • resampling
  • simultaneous confidence intervals


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