# Explain what changes would be required so that you could analyze the hypothesis using a chi-square test. For instance, rather than looking at test sco

Explain what changes would be required so that you could analyze the hypothesis using a chi-square test. For instance, rather than looking at test scores as a range from 0 to 100, you could change the variable to low, medium, or high. What advantages and disadvantages do you see in using this approach? Which is the better option for this hypothesis, the parametric approach or nonparametric approach?
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Step 1
The changes would be required so that you could analyze the hypothesis using a chi-square test are:
Step 2
In ANOVA, we have two or more group means that we have to compare.
In Chi square test, we have two categorical variables and want to determine whether one variable is related to the other variable.
Thus, for changing from ANOVA to chi square, rather than looking at test scores as a range from 0 to 100, we could change the variable to low, medium or high, so that it become categorical, thus amenable for chi square test.
Step 3
Advantages of chi-square test over ANOVA:
Chi-square test is robust with respect to the distribution of the data due to its non-parametric characteristic.
Disadvantages of chi-square test over ANOVA:
Chi-square test does not give much information about the strength of the relationship. It is sensitive to sample size. It is sensitive to to small expected frequencies in one or more cells in the ${\chi }^{2}$ table.
Step 4
Non-parametric test is better option for this hypothesis since data are not given as normally distributed and the non-parametric test which is distribution free is applicable