1. Final Test Information The final test is Monday, April 13 at 8:30 am The final test is Monday, April 13 at 8:30 am GRH102: Last name begins with A - I GRH102: Last name begins with A - I GRH106: Last name begins with K - Z GRH106: Last name begins with K - Z The exam is out of 40: 15 multiple choice (worth 15); 5 short answer (worth 25) The exam is out of 40: 15 multiple choice (worth 15); 5 short answer (worth 25) It will cover material from the entire course, but the focus will be on ANOVA and correlation It will cover material from the entire course, but the focus will be on ANOVA and correlation You will need to be able to identify when to do a z-test, t-test, ANOVA, and correlation You will need to be able to identify when to do a z-test, t-test, ANOVA, and correlation Not all of the short-answer questions require calculations Not all of the short-answer questions require calculations

2. Statz Rappers Statz Rappers Statz Rappers Statz Rappers

3. The Basics of Hypothesis Testing 1.State the null and research hypotheses in words and symbols 2.Determine the level of significance 3.Identify the critical value 4.Calculate the test statistic 5.Evaluate the test statistic in light of the critical value 6.Make a decision about the null hypothesis 7.State your conclusions in plain language

4. Statistical Tests: Single sample mean Used to compare a single sample to a population Used to compare a single sample to a population Is the sample significantly different from the population? Is the sample significantly different from the population? H 0 : µ = X H 1 : µ ≠ X If you know the population standard deviation, then use a z-test If you know the population standard deviation, then use a z-test If you do not know the population standard deviation, then use a t-test If you do not know the population standard deviation, then use a t-test

5. Statistical Tests: T-test for two related samples Used to compare two groups that are related in some way Used to compare two groups that are related in some way Are the two groups significantly different from each other? Are the two groups significantly different from each other? H 0 : µ D = 0 H 0 : µ D = 0 H 1 : µ D ≠ 0 H 1 : µ D ≠ 0 Could be a pre/post (i.e., before/after) design Could be a pre/post (i.e., before/after) design Could be two groups whose participants are related in some way (couples, twins, siblings etc). Could be two groups whose participants are related in some way (couples, twins, siblings etc).

6. Statistical Tests: T-test for two independent samples Used to compare two groups that are independent of each other Used to compare two groups that are independent of each other Are the two groups significantly different from each other? Are the two groups significantly different from each other? H o : µ 1 = µ 2 H o : µ 1 = µ 2 H 1 : µ 1 ≠ µ 2 H 1 : µ 1 ≠ µ 2

7. Statistical Tests: Analysis of variance Used to compare three or more groups Used to compare three or more groups Are the groups significantly different from each other? Are the groups significantly different from each other? H o : µ 1 = µ 2 = µ 3 H o : µ 1 = µ 2 = µ 3 H 1 : µ 1 ≠ µ 2 ≠ µ 3 H 1 : µ 1 ≠ µ 2 ≠ µ 3 If you reject your null hypothesis and conclude that there is a difference between the groups, you need to conduct post hoc (Tukey HSD) tests to determine which groups are different If you reject your null hypothesis and conclude that there is a difference between the groups, you need to conduct post hoc (Tukey HSD) tests to determine which groups are different

8. ANOVA Summary Table SourcedfSSMS F BetweenK-1MS B *df B SS B /df B MS B /MS W WithinN-KMS W *df W SS W /df W TotalN-1SS B + SS W

9. Statistical Tests: Correlation Used to determine if there is a relationships between two variables Used to determine if there is a relationships between two variables Are the variables significantly correlated with each other? Are the variables significantly correlated with each other? H 0 : r = 0 H 0 : r = 0 H 0 : r ≠ 0 H 0 : r ≠ 0 There are three ways to describe the relationship between the variables: There are three ways to describe the relationship between the variables: The direction and strength of the relationship (i.e., is r positive or negative, and how close is it to 1.0?) The direction and strength of the relationship (i.e., is r positive or negative, and how close is it to 1.0?) Is r significant (compare obtained value of r to critical value)? Is r significant (compare obtained value of r to critical value)? The amount of variance in one variable explained by the other (i.e., r 2, the coefficient of determination) The amount of variance in one variable explained by the other (i.e., r 2, the coefficient of determination)

10. Study Designs Experimental Looks for differences between groups of scores Looks for differences between groups of scores Uses terms like “effect”, “difference”, “cause” Uses terms like “effect”, “difference”, “cause” Use z-test, t-test or ANOVA to analyze data Use z-test, t-test or ANOVA to analyze data Correlational Looks for relationships between groups of scores Uses terms like “relationship”, “correlation” Use correlation to analyze data

11. Data Analysis Decision Tree for Experimental Designs How many groups? 2 groups Do a t-test 2 groups Do a t-test 3 or moregroups Do an ANOVA 3 or moregroups Do an ANOVA Are the scores related? Is F obt ≥ F crit ? No T-test for independent samples No T-test for independent samples Yes T-test for related samples Yes T-test for related samples No Do not reject H 0 No Do not reject H 0 Yes Reject H 0 Conduct HSD post hoc test Yes Reject H 0 Conduct HSD post hoc test 1 group (compare sample to population) Do you know the population standard deviation? No T-test for single samples No T-test for single samples Yes Z-test for single samples Yes Z-test for single samples