Parametric vs non-parametric tests:

Generally, parametric tests are more powerful than non-parametric tests.

Parametric tests make the assumption that data is normally distributed. Non-parametric tests do not make this assumption.

Non-parametric tests look at the rank order of the values rather than absolute differences between them.

For the use of a parametric test, they data must be:

1) Normally distributed

2) Data should be of interval or ratio measurement

3) Homogeneity of variance

Homogeneity of variance:

The variance of the two populations that the sample is drawn from must have equal variances check variances of samples.

Guideline - if variance between samples is more than 4 times larger not equal parametric assumption is violated.

Directional/Nondirectional Tests:

“Two-tailed” or “one-tailed” test?. Publications need to state which was used as the two will produce a different p value for the same data set.

One tailed test is more powerful – less likely to commit a type 2 error. Two tailed tests are more conservative and may need a larger treatment effect to get the same level of statistical significance. One-tailed tests are usually used when the direction of the difference is known in advance.

Equivalent Parametric and parametric tests:

Unpaired t-test Mann-Whitney U test

Paired t-test Wilcoxon matched pairs test

One-way ANOVA Kruskall-Wallis

Two-way ANOVA Two-way ANOVA by ranks

Chi square Fisher’s exact test

Pearson’s r Spearman’s r

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