question : device questions on the following statements

"Contingency tables: Contingency tables highlight relationships between categorical variables (Tahata, 2022).

Contingency tables play important roles in various fields, as they highlight relationships between categorical variables. Typically, the analysis of contingency tables is interested in whether row and column variables are independent. If the independence hypothesis is rejected, then the association between the variables is of interest.

A contingency table showsthe observed frequency of two variables, the observed frequencies of which are arranged into rows and columns. The intersection of a row and a column of a contingency table is called a cell (Tahata, 2022).

How contingency tables and their related statistics are used to test for significance of relations among the data.

The chi-squared test of significance is useful as a tool to determine whether it is worth the researcher's effort tointerpret a contingency table. A significant result of Chi-squared test means that a contingency table should be interpreted. A non-significant test means that no effects were discovered, which means that an interpretation is not useful (Oliveira, 2018).

Two statistics that can be used in contingency tables are chi-square and FET. Distinguish between the two statistics

The chi-square: A chi-square test is a statistical test used to compare observed results with expected results. The purpose of this test is to determine if a difference between observed data and expected data is due to chance, or if it is due to a relationship between the variables you are studying. Therefore, a chi-square test is an excellent choice to help us better understand and interpret the relationship between two categorical variables (Weltman, 2021).

Fisher's exact test (FET): Fisher's exact test is astatistical testused to determine if there are nonrandom associations between twocategorical variables. Fisher's exact test is practically applied only in analysis of small samples but actually it is valid for all sample sizes (Kim, 2017).

Interpreting chi-square and Fisher exact test ( FET)

For a Chi-square test, ap-valuethat is less than or equal to yoursignificance level indicates there is sufficient evidence to conclude that the observed distribution is not the same as the expected distribution. You can conclude that a relationship exists between the categorical variables (Frost, 2017).

Fisher exact test would be interpreted as if there 'is or isn't' statistically significant association between the two variables."