Parametric tests assume data follow a defined distribution, such as a normal distribution, while relying on population parameters. On the other hand non non-parametric tests have no biases on the distribution of the data.
Some of the common parametric tests include
- One-sample t-test: Tests if the mean of a single group is equal to a known mean.
- Two-sample t-test: Compares the means of two independent groups.
- Paired t-test: Compares means from the same group at different times.
- ANOVA (Analysis of Variance): Compares means among three or more groups.
- Z-test: Tests if there is a difference between sample and population means when sample size is large.
Common non-parametric tests
- Mann-Whitney U test: Compares differences between two independent groups when the dependent variable is either ordinal or continuous, but not normally distributed.
- Wilcoxon signed-rank test: Compares two related samples, matched samples, or repeated measurements on a single sample to assess whether their population mean ranks differ.
- Kruskal-Wallis H test: Non-parametric alternative to ANOVA.
- Chi-square test: Tests relationships between categorical variables.
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