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Parametric versus non parametric test
- 1. Parametric VERSUS Non Parametric test BY JWANIKA VANSIYA
- 2. Parametric test • A statistical test, in which specific assumptions are made about the population parameter is known as parametric test. Parametric test
- 3. When to use parametric test? Four conditions have to be satisfied: When to use parametric test ????
- 4. . Data should be normally distributed.
- 5. . Variation in the results should be roughly same.. • Homogenecity of variances assessed by Levene’s test
- 6. Non parametric test • Nonparametric tests are also called distribution-free tests because they don’t assume that your data follow a specific distribution. Non parametric test
- 7. When to use non parametric test?
- 8. Parametric test or Non parametric test-Determination
- 10. Parametric versus Non Parametric test. Parametric test Specific assumptions are made regarding the population Parametric test is powerful if it is exists Test statistics based on distribution Non parametric test No specific assumptions are made regarding the population Not powerful like parametric test Test statistics is arbitrary
- 11. . Parametric test No parametric test exists for nominal scale data Central measure - mean Can draw more conclusions Non parametric test Non parametric test exists for nominal scale data Central measure - median Simplicity , not affected by outliers
- 12. Parametric versus non parametric test Study type Parametric test Non parametric test Compare means between two distinct/independent groups Two-sample t-test Mann- whitney test Compare two quantitative measurements taken from the same individual Paired t-test Wilcoxon signed-rank test Compare means between three or more distinct/independent groups Analysis of variance (ANOVA) Kruskal-Wallis test
- 13. Study type Parametric test Non parametric test Repeated measures, >2 conditions One-way, repeated measures ANOVA Friedman's test Estimate the degree of association between two quantitative variables Pearson coefficient of correlation Spearman’s rank correlation
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