The Kruskal‐Wallis (Kruskal & Wallis, 1952) is a nonparametric statistical test that assesses the differences among three or more independently sampled groups on a single, non‐normally distributed continuous variable. Non‐normally distributed data (e.g., ordinal or rank data) are suitable for the Kruskal‐Wallis test. In contrast, the one‐way analysis of variance (ANOVA), which is a parametric test, may be used for a normally distributed continuous variable. The Kruskal‐Wallis test is an extension of the two‐group Mann‐Whitney U (Wilcoxon rank) test. Thus, the Kruskal‐Wallis is a more generalized form of the Mann‐Whitney U test and is the nonparametric version of the one‐way ANOVA.
The Mann‐Whitney U test, which is also known as the Wilcoxon rank sum test, tests for differences between two groups on a single, ordinal variable with no specific distribution (Mann & Whitney, 1947; Wilcoxon, 1945). In contrast, the independent samples t‐test, which is also a test of two groups, requires the single variable to be measured at the interval or ratio level, rather than the ordinal level, and to be normally distributed. We accordingly refer to the Mann‐Whitney U test as the nonparametric version of the parametric t‐test. Both tests require two independently sampled groups and assess whether two groups differ on a single, continuous variable. The two tests, however, differ on the assumed distribution. A nonparametric test assumes no specific distribution, whereas a parametric test assumes a specific distribution. Thus, the Mann‐Whitney U is conceptually similar to the t‐test for determining whether two sampled groups are from a single population. When data do not meet the parametric assumptions of the t‐test, the Mann‐Whitney U tends to be more appropriate.
Our present social sciences are at risk of losing sight of their primary purpose: the goal of reducing uncertainty. For years social scientists have drifted slowly toward the routine of employing of accepted methodological, conceptual, and analytical tools rather than engaging in problem oriented inquiry. Scientific contributions are reviewed in accordance to their compliance with the routine application of tools rather than focusing on their ability to problem-solve for a wider population. Researchers in every area of psychology for instance now insist on using methods such as random assignment and control groups, as well as data analytic procedures such as null hypothesis significance testing without regard to their relevance. A problem-focused inquiry would not dictate the routine use of any particular tool but rather the judicious application of tools when deemed appropriate. The following article describes the current situation in the framework contrasting toolbased and problem-focused inquiry and offers several insights that may create a more balanced and fruitful approach to scientific inquiry.
Our present social sciences are at risk of losing sight of their primary purpose: the goal of reducing uncertainty. For years social scientists have drifted slowly toward the routine of employing of accepted methodological, conceptual, and analytical tools rather than engaging in problem oriented inquiry. Scientific contributions are reviewed in accordance to their compliance with the routine application of tools rather than focusing on their ability to problem-solve for a wider population. Researchers in every area of psychology for instance now insist on using methods such as random assignment and control groups, as well as data analytic procedures such as null hypothesis significance testing without regard to their relevance. A problem-focused inquiry would not dictate the routine use of any particular tool but rather the judicious application of tools when deemed appropriate. The following article describes the current situation in the framework contrasting toolbased and problem-focused inquiry and offers several insights that may create a more balanced and fruitful approach to scientific inquiry.
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