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When do you use parametric and nonparametric tests?
If the mean more accurately represents the center of the distribution of your data, and your sample size is large enough, use a parametric test. If the median more accurately represents the center of the distribution of your data, use a nonparametric test even if you have a large sample size.
Do non parametric tests assume normality?
While nonparametric tests don’t assume that your data follow a normal distribution, they do have other assumptions that can be hard to meet. If your groups have a different spread, the nonparametric tests might not provide valid results.
What are the assumptions of non-parametric test?
The common assumptions in nonparametric tests are randomness and independence. The chi-square test is one of the nonparametric tests for testing three types of statistical tests: the goodness of fit, independence, and homogeneity.
How to choose between parametric and nonparametric tests?
There are two types of statistical tests that are appropriate for continuous data — parametric tests and nonparametric tests. Parametric tests are suitable for normally distributed data. Nonparametric tests are suitable for any continuous data, based on ranks of the data values.
How can I test my data for normality?
Test the data for normality – if your data is normally distributed, then it meets the criteria for the CLM no matter how little data you have and you can use parametric tests. Tests for normality can be found in “ Single Variable Analyses ”
Which is an example of a non-parametric distribution?
A Poisson distribution with a rate of 5 is another example that requires only one parameter, and again this one parameter is sufficient to fully describe that particular Poisson distribution. Moreover, any two distributions of the same type with the same parameters are identical distributions.
When to use a parametric test in a hypothesis test?
When we assume that the distribution of some variable (like heights of men in inches) follows a well-known distribution (like a normal distribution), that can be boiled down to knowledge of just a couple of parameters (like mu and sigma), and then we use that in conducting a hypothesis test, we are using a parametric test.