Non-parametric tests
 

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Wilcoxon signed rank test . To test difference between paired data. STEP 1. Exclude any differences which are zeroPut the rest of differences in ascending orderIgnore their signsAssign them ranksIf any differences are equal, average their ranks. STEP 2. Count up the ranks of ives as T
Non-parametric tests

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1. Non-parametric tests Note: When valid use parametric Commonly used Wilcoxon Chi square etc. Performance comparable to parametric Useful for non-normal data If normalization not possible Note: CI derivation-difficult/impossible

2. Wilcoxon signed rank test To test difference between paired data

3. STEP 1 Exclude any differences which are zero Put the rest of differences in ascending order Ignore their signs Assign them ranks If any differences are equal, average their ranks

4. STEP 2 Count up the ranks of +ives as T+ Count up the ranks of –ives as T-

5. STEP 3 If there is no difference between drug (T+) and placebo (T-), then T+ & T- would be similar If there were a difference one sum would be much smaller and the other much larger than expected The smaller sum is denoted as T T = smaller of T+ and T-

6. STEP 4 Compare the value obtained with the critical values (5%, 2% and 1% ) in table N is the number of differences that were ranked (not the total number of differences) So the zero differences are excluded

8. Wilcoxon rank sum test To compare two groups Consists of 3 basic steps

9. Non-parametric equivalent of t test

10. Step 1 Rank the data of both the groups in ascending order If any values are equal average their ranks

11. Step 2 Add up the ranks in group with smaller sample size If the two groups are of the same size either one may be picked T= sum of ranks in group with smaller sample size

12. Step 3 Compare this sum with the critical ranges given in table Look up the rows corresponding to the sample sizes of the two groups A range will be shown for the 5% significance level



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