Inferential statistics suggest statements or make predictions about a population based on a sample from that population. Non-parametric tests relate to data that are flexible and do not follow a normal distribution.
They are also known as “distribution-free” and the data are generally ranked or grouped. Non-parametric data are lacking those same parameters and cannot be added, subtracted, multiplied, or divided. These data include nominal measurements such as gender or race; or ordinal levels of measurement such as IQ scales,or survey response categories such as “good, better, best”, “agree, neutral, disagree”, etc.
Examples of non-parametric inferential tests include ranking, the chi-square test, binomial test and Spearman's rank correlation coefficient.
- Parametric vs. Non-parametric tests. This web page provides a table which demonstrates the various differences between parametric and non-parametric tests
Price, J., & Chamberlayne, D. W. (2008). Descriptive and Multivariate Statistics. IACA. www.iaca.net/ExploringCA/2Ed/exploringca_chapter9.pdf
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