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What is the difference between parametric and non-parametric tests in hypothesis testing?
The difference between parametric and non-parametric tests in hypothesis testing as follows.
Parametric Tests: These tests assume that the data follows a known distribution, typically normal distribution, and rely on specific parameters like mean and variance. For example,include t-test and ANOVA.
Non-Parametric Tests: These tests do not assume any particular distribution. They are often used when the data doesn't meet the assumptions of parametric tests, such as with ordinal data or non-normal distributions. For example,include Chi-square test and Mann-Whitney U test. For example,If you want to compare the means of two normally distributed groups, you would use a t-test (parametric). However, if the data is skewed or ordinal, you might choose a Mann-Whitney U test (non-parametric) instead.
Parametric Tests: These tests assume that the data follows a known distribution, typically normal distribution, and rely on specific parameters like mean and variance. For example,include t-test and ANOVA.
Non-Parametric Tests: These tests do not assume any particular distribution. They are often used when the data doesn't meet the assumptions of parametric tests, such as with ordinal data or non-normal distributions. For example,include Chi-square test and Mann-Whitney U test. For example,If you want to compare the means of two normally distributed groups, you would use a t-test (parametric). However, if the data is skewed or ordinal, you might choose a Mann-Whitney U test (non-parametric) instead.