Which statistical test to use?

The choice of statistical test depends on the specific research question, the type of data being analyzed, and the assumptions of the statistical test. Here are some commonly used statistical tests for different scenarios:

1. Independent t-test:

Used to compare means between two independent groups.

2. Paired t-test:

Used to compare means between two related groups (e.g., pre-test and post-test measurements).

3. Analysis of Variance (ANOVA):

Used to compare means between more than two independent groups.

4. Chi-square test:

Used to determine if there is a significant association between two categorical variables.

5. Pearson correlation:

Used to measure the strength and direction of the linear relationship between two continuous variables.

6. Linear regression:

Used to model the relationship between a dependent variable and one or more independent variables.

7. Mann-Whitney U test:

Used to compare medians between two independent groups when the assumptions for the t-test are not met.

8. Kruskal-Wallis test:

Used to compare medians between more than two independent groups when the assumptions for ANOVA are not met.

9. Wilcoxon signed-rank test:

Used to compare medians between two related groups when the assumptions for the paired t-test are not met.

10. Spearman correlation:

Used to measure the strength and direction of the monotonic relationship between two continuous or ordinal variables.

These are just a few examples, and there are many other statistical tests available depending on the specific research question and data characteristics. It is important to consult with a statistician or use statistical software to determine the most appropriate test for your analysis.

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