1) T-test (tea for two) = looks at the mean values of 2 groups
2) ANOVA (analysis of variance) ~ like t-test but = looks at mean values of 3 or more groups
3) Chi-square = looks at the (%) or proportions between 2 or more groups.
so, just look for how many groups being addressed and what values they are using (% or means)
Despite a numeric value (blood pressure) being measured, patients would be designated either hypertensive or normotensive. To my understanding, the best test for comparing categorical variables across groups is a chi square test.
In contrast, a t-test is used to compare between the means of two groups (measured variable must be quantitative).
I was never taught what t-test vs Chi-squared was. Going off of the FA table didn't help. This video below explained the concept really well.
For the Q refer to @mc1's comment.
1) Analysis of variance is a procedure used for comparing sample means to see if there is sufficient evidence to infer that the means of the corresponding population distributions also differ.
2) Where t-test compare only two distributions, analysis of variance is able to compare many. • What does the one-way part mean? It is one dependent variable (always continuous) and exactly one independent variable (always categorical). A single independent variable can have many levels.
Why is the correct answer chi-square test? I get that it’s used for categorical variables but we’re comparing prevalence percentages. Is that considered categorical?