Have a personal or library account? Click to login
Why Psychologists Should by Default Use Welch’s t-test Instead of Student’s t-test Cover

Why Psychologists Should by Default Use Welch’s t-test Instead of Student’s t-test

International Review of Social Psychology
Volume 30 (2017): Issue 1
By: Marie Delacre,  Daniël Lakens and  Christophe Leys  
Open Access
|Apr 2017

References

  1. 1
    Baguley T. Serious stats: A guide to advanced statistics for the behavioral sciences 2012 Palgrave Macmillan Retrieved from https://books.google.fr/books?hl=fr&lr=&id=ObUcBQAAQBAJ&oi=fnd&pg=PP1&dq=baguley+2012&ots=-eiUlHiCYs&sig=YUUKZ7jiGF33wdo3WVO-8l-OUu8
    Back to article
  2. 2
    Banerjee A. Chitnis U. Jadhav S. Bhawalkar J. Chaudhury S. Hypothesis testing, type I and type II errors Industrial Psychiatry Journal 2009 18 2 127 10.4103/0972-6748.62274
    Back to article
  3. 3
    Baumgartner H. Steenkamp J.-B. E. Response styles in marketing research: A cross-national investigation Journal of Marketing Research 2001 38 2 143 156 10.1509/jmkr.38.2.143.18840
    Back to article
  4. 4
    Bryk A. S. Raudenbush S. W. Heterogeneity of variance in experimental studies: A challenge to conventional interpretations Psychological Bulletin 1988 104 3 396 10.1037/0033-2909.104.3.396
    Back to article
  5. 5
    Carroll R. J. Schneider H. A note on Levene’s tests for equality of variances Statistics & Probability Letters 1985 3 4 191 194 10.1016/0167-7152(85)90016-1
    Back to article
  6. 6
    Cowdry R. W. Gardner D. L. O’Leary K. M. Leibenluft E. Rubinow D. R. Mood variability: A study of four groups American Journal of Psychiatry 1991 148 11 1505 1511 10.1176/ajp.148.11.1505
    Back to article
  7. 7
    Cumming G. Understanding the new statistics: Effect sizes, confidence intervals, and meta-analysis 2013 Routledge Retrieved from https://books.google.fr/books?hl=fr&lr=&id=1W6laNc7Xt8C&oi=fnd&pg=PR1&dq=understanding+the+new+statistics:+effect+sizes,+confidence+intervals,+and+meta-analysis&ots=PuJZVHb03Q&sig=lhSjkfzp4o5OXAKhZ_zYzP9nsr8
    Back to article
  8. 8
    Erceg-Hurn D. M. Mirosevich V. M. Modern robust statistical methods: An easy way to maximize the accuracy and power of your research American Psychologist 2008 63 7 591 10.1037/0003-066X.63.7.591
    Back to article
  9. 9
    Feingold A. Sex differences in variability in intellectual abilities: A new look at an old controversy Review of Educational Research 1992 62 1 61 84 10.3102/00346543062001061
    Back to article
  10. 10
    Glass G. V. Peckham P. D. Sanders J. R. Consequences of failure to meet assumptions underlying the fixed effects analyses of variance and covariance Review of Educational Research 1972 42 3 237 288 10.3102/00346543042003237
    Back to article
  11. 11
    Grissom R. J. Heterogeneity of variance in clinical data Journal of Consulting and Clinical Psychology 2000 68 1 155 10.1037/0022-006X.68.1.155
    Back to article
  12. 12
    Hanushek E. A. Wößmann L. Does educational tracking affect performance and inequality? Differences- in-differences evidence across countries* Economic Journal 2006 116 510 C63 C76 10.1111/j.1468-0297.2006.01076.x
    Back to article
  13. 13
    Hayes A. F. Cai L. Further evaluating the conditional decision rule for comparing two independent means British Journal of Mathematical and Statistical Psychology 2007 60 2 217 244 10.1348/000711005X62576
    Back to article
  14. 14
    Henrich J. Heine S. J. Norenzayan A. Most people are not WEIRD Nature 2010 466 7302 29 29 10.1038/466029a
    Back to article
  15. 15
    Hoekstra R. Kiers H. Johnson A. Are assumptions of well-known statistical techniques checked, and why (not)? Frontiers in Psychology 2012 3 137 10.3389/fpsyg.2012.00137
    Back to article
  16. 16
    Keppel G. Design and analysis: A researcher’s handbook 1991 Prentice-Hall, Inc. Retrieved from http://psycnet.apa.org/psycinfo/1991-98751-000
    Back to article
  17. 17
    Keselman H. J. Huberty C. J. Lix L. M. Olejnik S. Cribbie R. A. Donahue B. Levin J. R. Statistical practices of educational researchers: An analysis of their ANOVA, MANOVA, and ANCOVA analyses Review of Educational Research 1998 68 3 350 386 10.3102/00346543068003350
    Back to article
  18. 18
    Keselman H. J. Othman A. R. Wilcox R. R. Fradette K. The new and improved two-sample t test Psychological Science 2004 15 1 47 51 10.1111/j.0963-7214.2004.01501008.x
    Back to article
  19. 19
    Kester S. W. The communication of teacher expectations and their effects on the achievement and attitudes of secondary school pupils 1969 University of Oklahoma Retrieved from https://shareok.org/handle/11244/2570
    Back to article
  20. 20
    Lakens D. Hilgard J. Staaks J. On the reproducibility of meta-analyses: Six practical recommendations BMC Psychology 2016 4 1 1 10.1186/s40359-016-0126-3
    Back to article
  21. 21
    Lim T.-S. Loh W.-Y. A comparison of tests of equality of variances Computational Statistics & Data Analysis 1996 22 3 287 301 10.1016/0167-9473(95)00054-2
    Back to article
  22. 22
    Luh W.-M. Guo J.-H. Approximate sample size formulas for the two-sample trimmed mean test with unequal variances British Journal of Mathematical and Statistical Psychology 2007 60 1 137 146 10.1348/000711006X100491
    Back to article
  23. 23
    Moser B. K. Stevens G. R. Homogeneity of variance in the two-sample means test American Statistician 1992 46 1 19 21 10.1080/00031305.1992.10475839
    Back to article
  24. 24
    Nachar N. The Mann-Whitney U: A test for assessing whether two independent samples come from the same distribution Tutorials in Quantitative Methods for Psychology 2008 4 1 13 20 10.20982/tqmp.04.1.p013
    Back to article
  25. 25
    Neuhäuser M. Ruxton G. D. Distribution-free two-sample comparisons in the case of heterogeneous variances Behavioral Ecology and Sociobiology 2009 63 4 617 623 10.1007/s00265-008-0683-4
    Back to article
  26. 26
    Nimon K. F. Statistical assumptions of substantive analyses across the general linear model: A mini-review Frontiers in Psychology 2012 3 322 10.3389/fpsyg.2012.00322
    Back to article
  27. 27
    Nordstokke D. W. Zumbo B. D. A Cautionary Tale about Levene’s Tests for Equal Variances Journal of Educational Research & Policy Studies 2007 7 1 1 14
    Back to article
  28. 28
    Overall J. E. Atlas R. S. Gibson J. M. Tests that are robust against variance heterogeneity in k × 2 designs with unequal cell frequencies Psychological Reports 1995 76 3 1011 1017 10.2466/pr0.1995.76.3.1011
    Back to article
  29. 29
    Rakotomalala R. Comparaison de populations Tests Non Paramétriques 2008 Université Lumiere Lyon, 2 Retrieved from http://www.academia.edu/download/44989200/Comp_Pop_Tests_Nonparametriques.pdf
    Back to article
  30. 30
    Rasch D. Kubinger K. D. Moder K. The two-sample t test: Pre-testing its assumptions does not pay off Statistical Papers 2011 52 1 219 231 10.1007/s00362-009-0224-x
    Back to article
  31. 31
    Ruxton G. D. The unequal variance t-test is an underused alternative to Student’s t-test and the Mann–Whitney U test Behavioral Ecology 2006 17 4 688 690 10.1093/beheco/ark016
    Back to article
  32. 32
    Shaw R. G. Mitchell-Olds T. ANOVA for unbalanced data: An overview Ecology 1993 74 6 1638 1645 10.2307/1939922
    Back to article
  33. 33
    Sheskin D. J. Handbook of parametric and nonparametric statistical procedures 2003 3rd ed. Boca Raton, Florida CRC Press 10.1201/9781420036268
    Back to article
  34. 34
    Shields S. Functionalism, Darwinism, and the psychology of women American Psychologist 1975 30 7 739 10.1037/h0076948
    Back to article
  35. 35
    Student The probable error of a mean Biometrika 1908 1 25 10.1093/biomet/6.1.1
    Back to article
  36. 36
    Wallenstein S. Zucker C. L. Fleiss J. L. Some statistical methods useful in circulation research Circulation Research 1980 47 1 1 9 10.1161/01.RES.47.1.1
    Back to article
  37. 37
    Wang H. Smith K. P. Combs E. Blake T. Horsley R. D. Muehlbauer G. J. Effect of population size and unbalanced data sets on QTL detection using genome-wide association mapping in barley breeding germplasm Theoretical and Applied Genetics 2012 124 1 111 124 10.1007/s00122-011-1691-8
    Back to article
  38. 38
    Welch B. L. The generalization of Student’s problem when several different population variances are involved Biometrika 1947 34 1/2 28 35 10.2307/2332510
    Back to article
  39. 39
    Wilcox R. R. Granger D. A. Clark F. Modern robust statistical methods: Basics with illustrations using psychobiological data Universal Journal of Psychology 2013 1 2 21 31
    Back to article
  40. 40
    Wilcox R. R. Keselman H. J. Modern robust data analysis methods: Measures of central tendency Psychological Methods 2003 8 3 254 10.1037/1082-989X.8.3.254
    Back to article
  41. 41
    Yuen K. K. The two-sample trimmed t for unequal population variances Biometrika 1974 61 1 165 170 10.1093/biomet/61.1.165
    Back to article
  42. 42
    Zimmerman D. W. Some properties of preliminary tests of equality of variances in the two-sample location problem Journal of General Psychology 1996 123 3 217 231 10.1080/00221309.1996.9921274
    Back to article
  43. 43
    Zimmerman D. W. Statistical significance levels of nonparametric tests biased by heterogeneous variances of treatment groups Journal of General Psychology 2000 127 4 354 364 10.1080/00221300009598589
    Back to article
  44. 44
    Zimmerman D. W. A note on preliminary tests of equality of variances British Journal of Mathematical and Statistical Psychology 2004 57 1 173 181 10.1348/000711004849222
    Back to article
  45. 45
    Zumbo B. D. Coulombe D. Investigation of the robust rank-order test for non-normal populations with unequal variances: The case of reaction time Canadian Journal of Experimental Psychology/Revue Canadienne de Psychologie Expérimentale 1997 51 2 139 10.1037/1196-1961.51.2.139
    Back to article
DOI: https://doi.org/10.5334/irsp.82 | Journal eISSN: 2397-8570
Journal RSS Feed
Language: English
Published on: Apr 5, 2017
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
Welch’s t-test,
Student’s t-test,
homogeneity of variance,
Levene’s test,
Homoscedasticity,
statistical power,
type 1 error,
type 2 error

© 2017 Marie Delacre, Daniël Lakens, Christophe Leys, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.

Previous articleVolume 30 (2017): Issue 1Next article