The Chi-square statistical test is used to compare observed data with the data we expect to attain according to the given hypothesis. The Chi-square tests are reliable in testing the likelihood that the observed distribution is due to chance. The Chi-square is commonly referred to as the goodness of fit since it examines how observed data fits within the distribution that is highly expected in the case where variables are independent ( Moore, Notz & Fligner, 2013) . Chi-squares are suitable for determining whether there is a difference between the observed frequencies and the expected frequencies. Chi-square provides the right grounds for admission or rejection of the null hypothesis indicating that data are independent.
The goodness of fit is used to determine the similarity or difference between the observed and the expected results. The student t-square is also used to form the basis for the admission or the rejection of the null hypothesis. The student t-test enables the statisticians to answer the fundamental questions as to whether two groups are statistically separate from one another. According to Moore, Notz & Fligner (2013), w henever one rejects the null hypothesis using the student t-test, therefore, it means that one is saying that the means are statistically different from each other. Differently, Chi-square tests as to whether there is a relationship between two different variables; however, it does not indicate the magnitude of the difference. Rejecting a null-hypothesis using the chi-square by extension means that there is a similarity between the two variables ( Moore, Notz & Fligner, 2013) .
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There is confusion when testing data whether to use the chi-square method or to use the student t-testing method. However, student t-tests work best with normally distributed information, whereas the chi-square is used mainly when handling nonparametric data. The T-tests are used majorly for the testing of means while chi-square tests the difference in categories indicating different levels of measurements ( Moore, Notz & Fligner, 2013) . For example, the student t-tests would reliably prove the similarity in reactions of groups of people to a particular product in the market. Chi-square would best test whether the response towards one product is similar or different from the response to competitor products in the same market.
References
Moore, D. S., Notz, W., & Fligner, M. A. (2013). The basic practice of statistics . WH Freeman.
