Encyclopedia of Clinical Neuropsychology

Living Edition
| Editors: Jeffrey Kreutzer, John DeLuca, Bruce Caplan

False Discovery Rate

Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-56782-2_9057-2

Synonyms

Definition

False Discovery Rate (FDR) is a statistical procedure employed to correct for multiple comparisons. Development was initially motivated in part as an alternative to the use of conservative familywise error rate (FWER) corrections that failed to identify marked effects in high-dimensional datasets that contained few cases (see Benjamini and Hochberg 1995). The False Discovery Rate is defined as the expected proportion of errors committed by falsely rejecting the null hypothesis (Benjamini and Hochberg 1995; Lin and Lee 2015). While originally implemented in behavioral genetics research (Benjamini et al. 2001; Goeman and Solari 2014), it has recently been introduced to the neuroimaging literature as an alternative to popular...

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References and Readings

  1. Benjamini, Y., & Gavrilov, Y. (2009). A simple forward selection procedure based on false discovery rate control. Ann. Appl. Stat., 3(1), 179–198.  https://doi.org/10.1214/08-AOAS194.CrossRefGoogle Scholar
  2. Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological), 57(1), 289–300.Google Scholar
  3. Benjamini, Y., Drai, D., Elmer, G., Kafkafi, N., & Golani, I. (2001). Controlling the false discovery rate in behavior genetics research. Behavioural Brain Research, 125(1–2), 279–284.CrossRefGoogle Scholar
  4. Genovese, C. R., Lazar, N. A., & Nichols, T. (2002). Thresholding of statistical maps in functional neuroimaging using the false discovery rate. NeuroImage, 15(4), 870–878.  https://doi.org/10.1006/nimg.2001.1037.CrossRefPubMedGoogle Scholar
  5. Glickman, M. E., Rao, S. R., & Schultz, M. R. (2014). False discovery rate control is a recommended alternative to Bonferroni-type adjustments in health studies. Journal of Clinical Epidemiology, 67(8), 850–857.  https://doi.org/10.1016/j.jclinepi.2014.03.012.CrossRefPubMedGoogle Scholar
  6. Goeman, J. J., & Solari, A. (2014). Multiple hypothesis testing in genomics. Statistics in Medicine, 33(11), 1946–1978.  https://doi.org/10.1002/sim.6082.CrossRefPubMedGoogle Scholar
  7. Li, D., Xie, Z., Zand, M., Fogg, T., & Dye, T. (2017). Bon-EV: An improved multiple testing procedure for controlling false discovery rates. BMC Bioinformatics, 18(1), 1.  https://doi.org/10.1186/s12859-016-1414-x.CrossRefPubMedPubMedCentralGoogle Scholar
  8. Lin, Y. T., & Lee, W. C. (2015). Importance of presenting the variability of the false discovery rate control. BMC Genetics, 16, 97.  https://doi.org/10.1186/s12863-015-0259-z.CrossRefPubMedPubMedCentralGoogle Scholar
  9. Madar, V., & Batista, S. (2016). FastLSU: A more practical approach for the Benjamini-Hochberg FDR controlling procedure for huge-scale testing problems. Bioinformatics, 32(11), 1716–1723.  https://doi.org/10.1093/bioinformatics/btw029.CrossRefPubMedGoogle Scholar
  10. Tak, S., & Ye, J. C. (2014). Statistical analysis of fNIRS data: A comprehensive review. NeuroImage, 85(Pt 1), 72–91.  https://doi.org/10.1016/j.neuroimage.2013.06.016.CrossRefPubMedGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Psychology and Neuroscience CenterBrigham Young UniversityProvoUSA