Chi-Square tests are nonparametric tests. Discuss the following: What are the differences between parametric tests and nonparametric tests? What are the requirements for Chi-Square tests? What are the limitations of Chi-Square tests?

Chi-Square tests are nonparametric tests. Discuss the following: What are the differences between parametric tests and nonparametric tests? What are the requirements for Chi-Square tests? What are the limitations of Chi-Square tests?

Question
Chi-square tests
asked 2021-01-19
Chi-Square tests are nonparametric tests. Discuss the following:
What are the differences between parametric tests and nonparametric tests?
What are the requirements for Chi-Square tests?
What are the limitations of Chi-Square tests?

Answers (1)

2021-01-20
Chi-square tests are non parametric tests
Difference between parametric and nonparametric tests:
Parametric tests make assumptions about the parameters of the population distribution from which the sample is drawn. This is often the assumption that the population data are normally distributed. Nonparametric tests don’t require that your data follow the normal distribution. They’re known as distribution-free tests and, as such, can be used for non-Normal variables.
Requirements for Chi-Square tests:
The sampling method should be simple random sampling. The variable under study should be categorical. The expected value of the number of sample observations in each level of the variable should be at least 5.
Limitations of Chi-Square tests:
All participants measured must be independent, means that an individual cannot fit in more than one category. The data must be frequency data The expected value of the number of sample observations in each level of the variable should be at least 5. Chi-square not be used if the sample size is less than 50
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