inferential-statistics

To choose the right inferential statistical test, you can use a decision tree that evaluates your data types, group counts, and paired designs. This approach considers continuous and categorical outcomes to recommend appropriate tests like t-tests, ANOVA, or non-parametric alternatives.

Hypothesis testing in Python requires checking assumptions like normality and variance homogeneity before execution. The process applies test-selection logic to verify these assumptions and automatically substitutes parametric tests with non-parametric alternatives if your data violates these requirements.

The best way to calculate effect size and post-hoc analyses after ANOVA is to use dedicated statistical libraries that compute standardized metrics and apply multiple-comparison corrections. This provides accurate pairwise comparisons and effect size measurements following significant omnibus tests.

Yes, you need to check data normality before running a t-test to determine if a parametric or non-parametric test is appropriate. Assumption checks evaluate your data distribution and guide the substitution of parametric tests with non-parametric alternatives when normality is violated.

Yes, you can perform inferential statistics on a CSV file by specifying your dependent variable and grouping factors. The analysis handles continuous and categorical outcomes across two or more groups, supporting both independent and paired experimental designs for comprehensive hypothesis testing.