statistical results: answering the research question
End et al. (2010) analyzed test measures and critical notes inclusion using chi-square contingency tests. A chi-square test is a statistical hypothesis test that is used when the variables of interest are nominal(represent different categories). Chi-square contingency tests (also known as chi-square tests for independence) are used to examine whether the distribution of frequencies over the categories of one nominal variable are independent, or unrelated to, the distribution of frequencies over the categories of a second nominal variable.
By focusing on any discrepancies between the experimental observed frequencies, and their corresponding expected frequencies (frequencies expected if no relationship existed among nominal variables), we can determine whether a contingent relationship exists between two or more nominal, or categorical variables. The null hypothesis (a statement dictating the lack of a relationship between two qualitative variables) can be rejected if the calculated chi-square value for observed frequency is greater than the critical chi-square value determined by the null hypothesis.
End et al. reported that the chi-squares for test performance and notes inclusion were both statistically significant, meaning that some correlation does exist between auditory cell phone distraction and academic performance.
