Chi-Square is a statistical test used to compare observed data with the data the researcher expects to find in relation to a specified hypothesis. The test is used to determine whether deviations between observed and expected data occurred simply by chance or are caused by other factors (Brooks, 2008). Chi square is generally used to test the null hypothesis. For example, it can be used to test whether there is no significant difference between expected and observed results. Chi square is used in two circumstances as below: i) When the researcher wishes to estimate how well the observed distribution matches the proportions. it's planned. This is called the “goodness of fit” test. ii) When the researcher wishes to estimate whether the random variables used are independent. Chi-square test assumptions: i) To use the chi-square test for independence, both variables used must be categorical data, i.e. the data must be measured at nominal or ordinal levels . In addition, the two variables used must be composed of at least two categorical and independent groups (Brooks, 2008). For example, ethnicity might include two groups (Hispanic, Caucasian, and American) and gender might include two groups of female and male. ii) When using chi square, the data should not be correlated. Therefore, the test cannot be performed when the data used in the research is correlated. iii) The data must also be quantitative and the observations made must be independent. This means that chi square cannot be used when the data used in the research is qualitative. iv) The sample size should be large enough. This means that the sample size ...... middle of paper ...... hip between the two variables. A regression coefficient close to zero means that there is a weak relationship between the two variables. On the other hand, a regression coefficient close to 1 shows a strong relationship between the two variables. I will use the Chi-test to answer the study hypothesis. Indeed, the test is normally used when the researcher wishes to determine whether there are differences in categorical variables. For example, social characteristics such as religion, political differences, ethnic differences, etc. Therefore, I will propose two hypotheses. Then I will choose the significance level, calculate the test value and then compare it to the critical value. If the test value is less than the critical value, I will not reject the null hypothesis. However, if the test value is greater than the critical value, I will reject the null hypothesis..