Non- Parametric statistics

1. Non- Parametric statistics Chi-Square Mann-Whitney U-test Kruskal Wallis test

2. 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

3. 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

5. 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).

6. Examples of NP statistics Chi-Square test (x²)

7. 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.

8. Chi-Square test (x²) • On access of PU saved data set,

9. 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

10. Examples of NP statistics Mann-Whitney U test

11. According PU data set, Age * incidence

12. Examples of NP statistics Kruskal Wallis test

13. According PU data set, age * group of patients