Just remember:

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).

https://www.slideshare.net/adsarwar/anova-and-ttest

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.