Solution
Differences between Parametric and Nonparametric Tests
A parametric test is a test that will make assumptions related to the defining properties of the population distributions from which given data will be drawn. On the other hand, a non-parametric test is a statistical test that makes no assumptions related to the defining properties or the parameters of the population distributions from which given data is drawn (Derrick et al., 2020). In this regard, non-parametric tests refer to a null category mainly because statistical tests will assume one thing or another related to the properties of the source population. Parametric tests include tests such as the analysis of variance and T-tests. Such tests assume that the underlying populations are normally distributed (Derrick et al., 2020). Parametric tests also assume that an equal-interval scale is used in one’s measure. On the other hand, non-parametric tests are statistical tests that do not make assumptions related to the distribution of the source population. In this regard, non-parametric tests can be referred to as distribution-free tests (Schober & Vetter, 2020). In summary parametric tests that make assumptions related to a parameter of a population. On the other hand, non-parametric tests are tests that are utilized in the case of independent variables that might be non-metric (Schober & Vetter, 2020).
Type of Test to use when Analyzing Data
To determine if a parametric or nonparametric test should be utilized when analyzing data one should consider whether the sample size is large enough and whether the mean represents the center of distribution of data (Schober & Vetter, 2020). An accurately centered mean and a large enough sample size calls for a parametric test. On the other hand a median representing the center of distribution of given data a nonparametric test should be utilized even if the sample size is large enough (Schober & Vetter, 2020).
References
Derrick, B., White, P., & Toher, D. (2020). Parametric and Non-Parametric Tests for the Comparison of Two Samples Which Both Include Paired and Unpaired Observations. Journal of Modern Applied Statistical Methods, 18(1), 2–23. https://doi.org/10.22237/jmasm/1556669520
Schober, P., & Vetter, T. R. (2020). Nonparametric Statistical Methods in Medical Research. Anesthesia & Analgesia, 131(6), 1862–1863. https://doi.org/10.1213/ane.0000000000005101
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