Data Analyses When Sample Sizes Are Small: Modern Advances for Dealing With Outliers, Skewed Distributions, and Heteroscedasticity
- PMID: 30045651
- DOI: 10.1123/jab.2017-0269
Data Analyses When Sample Sizes Are Small: Modern Advances for Dealing With Outliers, Skewed Distributions, and Heteroscedasticity
Abstract
The paper reviews advances and insights relevant to comparing groups when the sample sizes are small. There are conditions under which conventional, routinely used techniques are satisfactory. But major insights regarding outliers, skewed distributions, and unequal variances (heteroscedasticity) make it clear that under general conditions they provide poor control over the type I error probability and can have relatively poor power. In practical terms, important differences among groups can be missed and poorly characterized. Many new and improved methods have been derived that are aimed at dealing with the shortcomings of classic methods. To provide a conceptual basis for understanding the practical importance of modern methods, the paper reviews some modern insights related to why methods based on means can perform poorly. Then some strategies for dealing with nonnormal distributions and unequal variances are described. For brevity, the focus is on comparing 2 independent groups or 2 dependent groups based on the usual difference scores. The paper concludes with comments on issues to consider when choosing from among the methods reviewed in the paper.
Keywords: ANOVA; distribution-free techniques; nonnormality; nonparametric methods; robust methods.
Similar articles
-
An Updated Guide to Robust Statistical Methods in Neuroscience.Curr Protoc. 2023 Mar;3(3):e719. doi: 10.1002/cpz1.719. Curr Protoc. 2023. PMID: 36971417
-
Sample size determinations for Welch's test in one-way heteroscedastic ANOVA.Br J Math Stat Psychol. 2014 Feb;67(1):72-93. doi: 10.1111/bmsp.12006. Epub 2013 Jan 14. Br J Math Stat Psychol. 2014. PMID: 23316952
-
A Guide to Robust Statistical Methods in Neuroscience.Curr Protoc Neurosci. 2018 Jan 22;82:8.42.1-8.42.30. doi: 10.1002/cpns.41. Curr Protoc Neurosci. 2018. PMID: 29357109 Review.
-
MODERN REGRESSION METHODS THAT CAN SUBSTANTIALLY INCREASE POWER AND PROVIDE A MORE ACCURATE UNDERSTANDING OF ASSOCIATIONS.Eur J Pers. 2012 May 1;26(3):165-174. doi: 10.1002/per.860. Eur J Pers. 2012. PMID: 23626402 Free PMC article.
-
A comprehensive review of group level model performance in the presence of heteroscedasticity: Can a single model control Type I errors in the presence of outliers?Neuroimage. 2017 Feb 15;147:658-668. doi: 10.1016/j.neuroimage.2016.12.058. Epub 2016 Dec 25. Neuroimage. 2017. PMID: 28030782 Free PMC article. Review.
Cited by
-
How previous experience shapes future affective subjective ratings: A follow-up study investigating implicit learning and cue ambiguity.PLoS One. 2024 Feb 9;19(2):e0297954. doi: 10.1371/journal.pone.0297954. eCollection 2024. PLoS One. 2024. PMID: 38335190 Free PMC article.
-
Pulling the lever in a hurry: the influence of impulsivity and sensitivity to reward on moral decision-making under time pressure.BMC Psychol. 2024 May 14;12(1):270. doi: 10.1186/s40359-024-01773-y. BMC Psychol. 2024. PMID: 38745341 Free PMC article.
-
Doing Experimental Psychological Research from Remote: How Alerting Differently Impacts Online vs. Lab Setting.Brain Sci. 2022 Aug 10;12(8):1061. doi: 10.3390/brainsci12081061. Brain Sci. 2022. PMID: 36009124 Free PMC article.
-
Little fast, little slow, should I stay or should I go? Adapting cognitive control to local-global temporal prediction across typical development.PLoS One. 2023 Feb 24;18(2):e0281417. doi: 10.1371/journal.pone.0281417. eCollection 2023. PLoS One. 2023. PMID: 36827315 Free PMC article.
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources
Other Literature Sources
