Parametric and non parametric tests for hypotheses testing.

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This presentation explores hypothesis testing, highlighting its purpose in data-driven decision-making and illustrating its application with population assumptions. It categorises statistical tests into parametric (e.g., Z-Test, T-Test, ANOVA) and nonparametric types (e.g., Chi-Square, Mann-Whitney), emphasising their assumptions and suitability for varied data types. Advantages of each test type, criteria for selection, and practical case studies are explained to enhance comprehension....