1. Making Comparisons All hypothesis testing follows a common logic of comparison Null hypothesis and alternative hypothesis – mutually exclusive – exhaustive Experimental design and control group “Republicans have higher income than Democrats”?

2. Methods of Making Comparisons Independent Variable Categorical measures (nominal or ordinal) Continuous measures (interval or ratio) Dependent Variable Categorical measures (nominal or ordinal) Cross-Tabulation & (Chapter 7) Chi- square (Chapter 10) Logistic Regression Continuous measures (interval or ratio) Compare Means & (Chapter 9) Dummy Variables (Chapter 8) Correlation & Linear Regression

3. Cross-tabulation Relationship between two (or more) variables – Joint frequency distribution – Contingency table Observations should be independent of each other – One person’s response should tell us nothing about another person’s response Mutually exclusive and exhaustive categories

4. Cross-tabulation If the null hypothesis is true, the independent variable has no effect on the dependent variable The expected frequency for each cell MaleFemaleTotal Pro-??20 Anti-??80 Total50 100

5. Expected Frequency of Each Cell Expected frequency in the ith row and the jth column ……… (E ij ) Total counts in the ith row ……… (T i ) Total counts in the jth column ……… (T j ) Total counts in the table ……… (N)

6. Inferences about Sample Means Hypothesis testing is an inferential process Using limited information to reach a general conclusion Observable evidence from the sample data Unobservable fact about the population Formulate a specific, testable research hypothesis about the population

7. Null Hypothesis no effect, no difference, no change, no relationship, no pattern, no … any pattern in the sample data is due to random sampling error

8. Errors in Hypothesis Testing Type I Error – A researcher finds evidence for a significant result when, in fact, there is no effect (no relationship) in the population. – The researcher has, by chance, selected an extreme sample that appears to show the existence of an effect when there is none. – The p-value identifies the probability of a Type I error.