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The effects of violations of assumptions underlying the t test.
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Citations
Cites
An Approximate Distribution of Estimates of Variance Components
Satterthwaite, F. E. (1946) · Biometrics Bulletin · doi:10.2307/3002019 · Cited by 4,373
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Glass, G. V., Peckham, P. D., & Sanders, J. R. (1972) · Review of Educational Research · doi:10.3102/00346543042003237 · Cited by 1,826
Comparing an Individual's Test Score Against Norms Derived from Small Samples
Crawford, J. R., & Howell, D. C. (1998) · The Clinical Neuropsychologist · doi:10.1076/clin.12.4.482.7241 · Cited by 1,264
Modern robust statistical methods: An easy way to maximize the accuracy and power of your research.
Erceg-Hurn, D. M., & Mirosevich, V. M. (2008) · American Psychologist · doi:10.1037/0003-066x.63.7.591 · Cited by 959
Analyzing ordinal data with metric models: What could possibly go wrong?
Liddell, T. M., & Kruschke, J. K. (2018) · Journal of Experimental Social Psychology · doi:10.1016/j.jesp.2018.08.009 · Cited by 805
p -Curve and Effect Size: Correcting for Publication Bias Using Only Significant Results
Simonsohn, U., Nelson, L. D., & Simmons, J. P. (2014) · Perspectives on Psychological Science · doi:10.1177/1745691614553988 · Cited by 717
Consequences of violating the independence assumption in analysis of variance.
Kenny, D. A., & Judd, C. M. (1986) · Psychological Bulletin · doi:10.1037/0033-2909.99.3.422 · Cited by 616
To test or not to test: Preliminary assessment of normality when comparing two independent samples
Rochon, J., Gondan, M., & Kieser, M. (2012) · BMC Medical Research Methodology · doi:10.1186/1471-2288-12-81 · Cited by 296
A Comparison of Parametric and Non-Parametric Methods Applied to a Likert Scale
Mircioiu, C., & Atkinson, J. (2017) · Pharmacy · doi:10.3390/pharmacy5020026 · Cited by 286
Conventional vs. Worker-Owned Organizations
Rhodes, S. R., & Steers, R. M. (1981) · Human Relations · doi:10.1177/001872678103401201 · Cited by 223
Conditions for the Effectiveness of a Preliminary Test of Variance
Markowski, C. A., & Markowski, E. P. (1990) · The American Statistician · doi:10.1080/00031305.1990.10475752 · Cited by 190
Testing the Equivalence of Translations of Widely Used Response Choice Labels
Keller, S. D., Ware, J. E., Gandek, B., Aaronson, N. K., Alonso, J., Apolone, G., Bjorner, J. B., Brazier, J., Bullinger, M., Fukuhara, S., et al. (1998) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(98)00084-5 · Cited by 105
Chebyshev’s Inequality for Nonparametric Testing with Small N and α in Microarray Research
Beasley, T. M., Page, G. P., Brand, J. P. L., Gadbury, G. L., Mountz, J. D., & Allison, D. B. (2004) · Journal of the Royal Statistical Society Series C: Applied Statistics · doi:10.1111/j.1467-9876.2004.00428.x · Cited by 15
The normal distribution is not normal in psychological data: Moving beyond parametric dogma
Ferr, H. (2025) · PLOS Mental Health · doi:10.1371/journal.pmen.0000403 · Cited by 7
Metadata
- Authors
- Boneau, C. A.
- Venue
- Psychological Bulletin
- Published
- 1960-01
- Volume
- 57
- Issue
- 1
- Pages
- 49-64
- ISSN
- 0033-2909 (ISSN-L)
- 1939-1455
- OpenAlex
- W2133738611
- Cited by
- 790
- References
- 1
- FWCI
- 21.11
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
