Skip to main content
Log in

Distribution-free two-sample comparisons in the case of heterogeneous variances

  • Methods
  • Published:
Behavioral Ecology and Sociobiology Aims and scope Submit manuscript

Abstract

Behavioral ecologists are often faced with a situation where they need to compare the central tendencies of two samples. The standard tools of the t test and Mann–Whitney U test (equivalent to the Wilcoxon rank-sum test) are unreliable when the variances of the groups are different. The problem is particularly severe when sample sizes are different between groups. The unequal-variance t test (Welch test) may not be suitable for nonnormal data. Here, we propose the use of Brunner and Munzel’s generalized Wilcoxon test followed by randomization to allow for small sample sizes. This tests whether the probability of an individual from one population being bigger than an individual from the other deviates from random expectation. This probability may sometimes be a more clear and informative measure of difference between the groups than a difference in more commonly used measures of central tendency (such as the mean). We provide a recipe for carrying out a statistical test of the null hypothesis that this probability is 50% and demonstrate the effectiveness of this technique for sample sizes typical in behavioral ecology. Although the test is not available in any commercial software package, it is relatively straightforward to implement for anyone with some programming ability. Furthermore, implementations in R and SAS are freely available on the internet.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Bauer DF (1972) Constructing confidence sets using rank statistics. J Am Stat Assoc 67:687–690

    Article  Google Scholar 

  • Brunner E, Munzel U (2000) The nonparametric Behrens–Fisher problem: asymptotic theory and a small sample approximation. Biom J 42:17–25

    Article  Google Scholar 

  • Brunner E, Munzel U (2002) Nichtparametrische Datenanalyse. Springer, Berlin

    Google Scholar 

  • Brunner E, Dette H, Munk A (1997) Box-type approximations in nonparametric factorial designs. J Am Stat Assoc 92:1494–1502

    Article  Google Scholar 

  • Büning H (1997) Robust analysis of variance. J Appl Stat 24:319–332

    Article  Google Scholar 

  • Chu PS, Wang J (1997) Tropical cyclone occurrences in the vicinity of Hawaii: are the differences between El Niño and non-El Niño years significant? J Climate 10:2683–2689

    Article  Google Scholar 

  • Cliff N (1996) Ordinal methods for behavioral data. Erlbaum, Mahway

    Google Scholar 

  • Day RW, Quinn GP (1989) Comparison of treatments after an analysis of variance in ecology. Ecol Monogr 59:433–463

    Article  Google Scholar 

  • Delaney HD, Vargha A (2002) Comparing several robust tests of stochastic equality with ordinally scaled variables and small to moderate sized samples. Psychol Methods 7:485–503

    Article  PubMed  Google Scholar 

  • Gould SJ (1996) Full house: the spread of excellence from Plato to Darwin. Harmony Books, New York

    Google Scholar 

  • Huang Y, Xu H, Calian V, Hsu JC (2006) To permute or not to permute. Bioinformatics 22:2244–2248

    Article  PubMed  CAS  Google Scholar 

  • Janssen A (1997) Studentized permutation tests for non-i.i.d. hypotheses and the generalized Behrens–Fisher problem. Stat Probab Lett 36:9–21

    Article  Google Scholar 

  • Julious SA (2005) Why do we use pooled variance analysis of variance. Pharm Stat 4:3–5

    Article  Google Scholar 

  • Kasuya E (2001) Mann–Whitney U test when variances are unequal. Anim Behav 61:1247–1249

    Article  Google Scholar 

  • Manly BFJ (2007) Randomization, bootstrap and Monte Carlo methods in biology, 3rd edn. Chapman & Hall, Boca Raton

    Google Scholar 

  • Margolis LG, Esch W, Holmes JC, Kuris AM, Shad GA (1982) The use of ecological terms in parasitology (report of an ad hoc committee of the American Society of Parasitologists). J Parasitol 68:131–133

    Article  Google Scholar 

  • McArdle BH, Anderson MJ (2004) Variance heterogeneity, transformations, and models of species abundance: a cautionary tale. Can J Fish Aquat Sci 61:1294–1302

    Article  Google Scholar 

  • Milton RC (1970) Rank-order probabilities: two-sample normal shift alternatives. Wiley, New York

    Google Scholar 

  • Munzel U, Hauschke D (2003) A nonparametric test for proving noninferiority in clinical trials with ordered categorical data. Pharm Stat 2:31–37

    Article  Google Scholar 

  • Neubert K, Brunner E (2007) A studentized permutation test for the non-parametric Behrens–Fisher problem. Comput Stat Data Analysis 51:5192–5204

    Article  Google Scholar 

  • Neuhäuser M (2002) Two-sample tests when variances are unequal. Anim Behav 63:823–825

    Article  Google Scholar 

  • Neuhäuser M, Lam FC (2004) Nonparametric approaches to detecting differentially expressed genes in replicated microarray experiments. In: Chen Y-PP (ed) Conferences in research and practice in information technology, vol 29. Australian Computer Society, Adelaide, Australia

    Google Scholar 

  • Neuhäuser M, Poulin R (2004) Comparing parasite numbers between samples of hosts. J Parasitol 90:689–691

    Article  PubMed  Google Scholar 

  • Neuhäuser M, Lösch C, Jöckel KH (2007) The Chen-Luo test in case of heteroscedasticity. Comput Stat Data Analysis 51:5055–5060

    Article  Google Scholar 

  • Reiczigel J, Zakarias I, Rózsa L (2005) A bootstrap test of stochastic equality of two populations. Am Stat 59:156–161

    Article  Google Scholar 

  • Rice WR, Gaines SD (1989) One-way analysis of variance with unequal variances. Proc Natl Acad Sci USA 86:8183–8184

    Article  PubMed  CAS  Google Scholar 

  • Ruxton GD (2006) The unequal variance t-test is an underused alternative to Student’s t-test and the Mann–Whitney U test. Behav Ecol 17:688–690

    Article  Google Scholar 

  • Sawilowsky SS, Blair RC (1992) A more realistic look at the robustness and type II error properties of the t test to departures from population normality. Psychol Bull 111:352–360

    Article  Google Scholar 

  • Troendle JF (2002) A likelihood ratio test for the nonparametric Behrens–Fisher problem. Biom J 44:813–824

    Article  Google Scholar 

  • Troendle JF (2005) Letter to the editor. Am Stat 59:279

    Article  Google Scholar 

  • Wilcox RR (2003) Applying contemporary statistical techniques. Elsevier Academic, San Diego

    Google Scholar 

  • Wilcox RR (2005) Introduction to robust estimation and hypothesis testing, 2nd edn. Elsevier Academic, San Diego

    Google Scholar 

  • Wilson JB (2007) Priorities in statistics, the sensitive feet of elephants and don’t transform data. Folia Geobot 42:161–167

    Article  Google Scholar 

  • Zimmerman DW, Zumbo BN (1993) Rank transformations and the power of the Student t-test and Welch t-test for non-normal populations. Can J Exp Psychol 47:523–539

    Article  Google Scholar 

Download references

Acknowledgment

We thank two reviewers for very useful comments on a previous draft.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Markus Neuhäuser.

Additional information

Communicated by L.Z. Garamszegi

Rights and permissions

Reprints and permissions

About this article

Cite this article

Neuhäuser, M., Ruxton, G.D. Distribution-free two-sample comparisons in the case of heterogeneous variances. Behav Ecol Sociobiol 63, 617–623 (2009). https://doi.org/10.1007/s00265-008-0683-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00265-008-0683-4

Keywords

Navigation