Chapter 8: Chi-Square

Chapter 8: Chi-Square New Vocabulary: 2 Chi-square 𝑋 : a test used with independent samples of nominal or ordinal level data Degrees of Freedom (df): the number of values that are “free to be unknown” Null Hypothesis: no relationship, association, or difference exists between the variables of interest Chi-Square Test -​ Independent and random samples -​ Dependent variable (aka outcome variable) is nominal or ordinal -​ Uses a 2x2 table -​ All cells in the table must have a value greater than 5 -​ If any values are below 5, use the Fisher Exact Test instead -​ If any of the cells in the table have greater than 5 but less than 10 expected observations, you need to do a Yates Continuity Correction -​ Note: Chi-square test does not tell you the direction of the relationship/difference, you have to infer that based on the data Degrees of Freedom (df) -​ The number of values that are “free to be unknown” once the row and column numbers are in a 2x2 table -​ All 2x2 tables have 1 degree of freedom -​ df = (rows - 1) x (columns - 1) -​ df = (2 - 1) x (2 - 1) = 1 Statistical Significance 2 -​ If the 𝑋 result has a significant p-value less than alpha (p < 0.05), then you reject the null hypothesis that the two variables are independent -​ Eg. there is an association between the time of surgery and the need for postoperative blood transfusions -​ If the 𝑋 result has a p-value greater than alpha (p > 0.05), then the result is statistically significant and you fail to reject the null hypothesis -​ Eg. postoperative transfusion rates are not significantly different for patients in the morning or evening 2