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Non- Parametric statistics. Chi-Square Mann-Whitney U-test Kruskal Wallis test. Used to answer research questions ranging from whether a relationship exists between 2 variables to groups differences on an outcome measure NPar. Tests assume distribution free. Parametric :

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## Non- Parametric statistics

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**Non- Parametric statistics**Chi-Square Mann-Whitney U-test Kruskal Wallis test**Used to answer research questions ranging from whether a**relationship exists between 2 variables to groups differences on an outcome measure • NPar. Tests assume distribution free**Parametric:**Assume normally distributed population Powerful Flexible Study effects of many independents*dep. Study the interaction between variables Shows: magnitude of significance, relationship, and direction. Non-Parametric: Distribution free Small samples Data skewed Unable to handle multivariate questions Parametric Vs Non Parametric statisticsNO CLEAR RULES WHEN ONE APPROACH PREFFERED**Assumptions of Non-Parametric statistics**• Frequency data • Adequate sample, at least sample size of (5) subjects • Measure independent of each other (no subject can be in more than one cell in the design, and no subject can be used more than once). • Basic theoretical structure of categorical variables remains (rationale of categorization).**Examples of NP statistics**Chi-Square test (x²)**Chi-Square test (x²)**• The impact of gender on Pressure Ulcer (PU) development . • The researcher studied whether PU incidence was different between males and females. • Hypothesis (H¹) : There is significant statistical difference in PU incidence between males and females. • H° : There is no significant statistical difference in PU incidence between males and females.**Chi-Square test (x²)**• On access of PU saved data set,**Chi-Square test (x²)**• Df (degrees of freedom): the extent to which values are free to vary in a given specific number of subjects and a total score**Examples of NP statistics**Mann-Whitney U test**Examples of NP statistics**Kruskal Wallis test

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