Distinguish between parametric and non-parametric tests. What is the importance of nonparametric tests? Explain. 

Parametric and non-parametric tests are two types of statistical tests used to analyze data and draw inferences about populations. The main difference between the two lies in the assumptions they make about the underlying data distribution.

1. Parametric Tests:
- Parametric tests assume that the data follows a specific probability distribution, usually a normal distribution.
- They require the data to meet certain assumptions, such as homogeneity of variance and independence of observations.
- Parametric tests use population parameters (e.g., mean, standard ____ __________ _______ _____ ___ ______ __________ ________ _______.
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