The chi-square is a statistical test used to compare observed data with data that the researcher expects to find with respect to a specific hypothesis. The test is used to determine whether deviations in observed data from expected data occurred by chance alone or are caused by other factors (Brooks, 2008). Chi-Square is usually used to test the null hypothesis. For example, it can be used to check whether there is any 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 this is expected. This is called the "goodness of fit" test. ii) When the researcher wishes to estimate whether the random variables used are independent. Assumptions of the Chi-square test: i) To use the Chi-square test for independence, the two variables used must be of categorical data, i.e. the data should be measured at nominal or ordinal level. Furthermore, the two variables used should be composed of at least two categorical and independent groups (Brooks, 2008). For example, ethnicity might be made up of two groups (Hispanic, Caucasian, and American), and gender might be made up of two groups of females and males. ii) When using the 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 that are made must be independent. This means that the 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… half the paper… is even 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 address the study hypothesis. This is because the test is normally used when the researcher wants to determine whether there are differences in categorical variables. For example, social characteristics such as religion, political differences, ethnic differences, etc. Therefore, I will put forward two hypotheses. Next, 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.