INFERENTIAL STATISTICS: COMMON TESTS

Chi-Squire Tests

• Chi-square test is an inferential statistics technique designed to test for significant relationships between two variables organized in a bivariate table.
• Chi-square requires no assumptions about the shape of the population distribution from which a sample is drawn. However, like all inferential techniques it assumes random sampling.
• It can be applied to variables measured at a nominal and/or an ordinal level of measurement.
• The research hypothesis (H1) proposes that the two variables are related in the population.
• The null hypothesis (H0) states that no association exists between the two cross-tabulated variables in the population, and therefore the variables are statistically independent.

Formula for computing chi-squire statistic

Where, O=observed frequency, and  E = expected frequency

• The essence of the test is to compare the observed frequencies with the frequencies expected for independence
• if the difference between observed and expected frequencies is large, then we can reject the null hypothesis of independence.
• Determining the Degrees of Freedom

df = (r – 1)(c – 1)
where, r = the number of rows and c = the number of columns