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Question:
Discuss the key differences between parametric and non-parametric methods in hypothesis testing and their respective advantages.

Solution:
Parametric methods assume underlying data distribution (commonly normal), leveraging parameters like mean and variance. These tests, such as t-tests, offer higher precision with larger sample sizes. Non-parametric methods, by contrast, make no such assumptions, making them robust for analyzing skewed or non-normal data. Examples include the Wilcoxon Signed-Rank Test. Parametric methods excel with larger datasets due to their efficiency, while non-parametric methods are versatile for smaller, irregular datasets, ensuring validity despite distribution constraints.

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