2. Introduction to Statistics for the Social Sciences SBS200, COMM200, GEOG200, PA200, POL200, or SOC200 Lecture Section 001, Fall 2015 Room 150 Harvill Building 10:00 - 10:50 Mondays, Wednesdays & Fridays. http://courses.eller.arizona.edu/mgmt/delaney/d15s_database_weekone_screenshot.xlsx

4. By the end of lecture today 11/4/15 Logic of hypothesis testing Steps for hypothesis testing Levels of significance (Levels of alpha) what does p < 0.05 mean? what does p < 0.01 mean? Two-sample t-tests Introduction to ANOVA

5. Before next exam (November 20 th ) Please read chapters 1 - 11 in OpenStax textbook Please read Chapters 2, 3, and 4 in Plous Chapter 2: Cognitive Dissonance Chapter 3: Memory and Hindsight Bias Chapter 4: Context Dependence

6. Homework Assignment Go to D2L - Click on “Interactive Online Homework Assignments” Complete Assignment 18: HW18-Hypothesis testing, t-tests Using Excel Due: Friday, November 6 th

7. Everyone will want to be enrolled in one of the lab sessions Labs continue this week, Project 3

8. Confidence Interval of 99% Has and alpha of 1% α =.01 Confidence Interval of 90% Has and alpha of 10% α =. 10 Confidence Interval of 95% Has and alpha of 5% α =.05 99%95%90% Area outside confidence interval is alpha Area in the tails is called alpha Area associated with most extreme scores is called alpha Critical statistic Critical Statistic Critical statistic Critical statistic Critical statistic Critical statistic

9. Rejecting the null hypothesis The result is “statistically significant” if: the observed statistic is larger than the critical statistic (which can be a ‘z” or “t” or “r” or “F” or x 2 ) observed stat > critical stat If we want to reject the null, we want our t (or z or r or F or x 2 ) to be big!! the p value is less than 0.05 (which is our alpha) p < 0.05 If we want to reject the null, we want our “p” to be small!! we reject the null hypothesis then we have support for our alternative hypothesis

10. .. A note on z scores, and t score: Difference between means Variability of curve(s) Difference between means Numerator is always distance between means (how far away the distributions are) Denominator is always measure of variability (how wide or much overlap there is between distributions) Variability of curve(s)

11. What happened? We ran more subjects: Increased n So, we decreased variability Easier to find effect significant even though effect size didn’t change Big sampleSmall sample This is the sample size

12. What happened? We ran more subjects: Increased n So, we decreased variability Easier to find effect significant even though effect size didn’t change Big sampleSmall sample This is variance for each sample (Remember, variance is just standard deviation squared) This is variance for each sample (Remember, variance is just standard deviation squared)

13. A survey was conducted to see whether men or women superintendents make more money 1.The independent variable is ________________ 2.The dependent variable is _________________ 3. Who made more money men or women? 4. Identify the two means and the observed t score 5. Identify the p value and state whether it is less than.05

14. A survey was conducted to see whether men or women superintendents make more money 1.37834 E-05 Equals.00001378 4 zeros 6.8917 E-06 Equals.0000068917 5 zeros Are both p values less than 0.05?

15. A survey was conducted to see whether men or women superintendents make more money 1.37834 E-05 Equals.00001378 4 zeros 6.8917 E-06 Equals.0000068917 5 zeros A note on scientific notation: “E-05” means move the decimal to the left 5 places E-06” means move the decimal to the left 6 places

16. A survey was conducted to see whether men or women superintendents make more money. The independent variable is a. nominal level of measurement b. ordinal level of measurement c. interval level of measurement d. ratio level of measurement correct

17. A survey was conducted to see whether men or women superintendents make more money. The dependent variable is a. nominal level of measurement b. ordinal level of measurement c. interval level of measurement d. ratio level of measurement correct

18. A survey was conducted to see whether men or women superintendents make more money. The independent variable is a. continuous and qualitative b. continuous and quantitative c. discrete and qualitative d. discrete and quantitative correct

19. A survey was conducted to see whether men or women superintendents make more money. The dependent variable is a. continuous and qualitative b. continuous and quantitative c. discrete and qualitative d. discrete and quantitative correct

20. A survey was conducted to see whether men or women superintendents make more money. This is a a. quasi, between subject design b. quasi, within subject design c. true, between subject design d. true, within subject design correct

21. A survey was conducted to see whether men or women superintendents make more money. This is a a. one-tailed test b. two-tailed test c. three-tailed test d. not enough information correct

22. A survey was conducted to see whether men or women superintendents make more money. The null hypothesis is a. men make more money b. women make more money c. no difference between amount of money made d. there is a difference between the amount of money made correct

23. A survey was conducted to see whether men or women superintendents make more money. If the null hypothesis was rejected we will conclude that a. men make more money b. women make more money c.no difference between amount of money made d. there is a difference between the amount of money made correct

24. A survey was conducted to see whether men or women superintendents make more money. A Type I error would be a. claiming men make more money, when they don’t b. claiming women make more money, when they don’t c.claiming no difference between amount of money made, when there is a difference d. claiming there is a difference between the amount of money made, when there is no difference correct

25. A survey was conducted to see whether men or women superintendents make more money. A Type II error would be a. claiming men make more money, when they don’t b. claiming women make more money, when they don’t c.claiming no difference between amount of money made, when there is a difference d. claiming there is a difference between the amount of money made, when there is no difference correct

26. An t-test was conducted, there were ___ men in the study and ___ women. a. 18; 21 b. 21; 18 c. 19; 19 d. 38; 38 Let’s try one correct

27. A t-test was conducted, which of the following best describes the results: a. t(37) = 2.02; p < 0.05 b. t(21) = 2.02; n.s. c. t(37) = 5.0; p < 0.05 d. t(21) = 5.0; n.s Let’s try one correct

28. A t-test was conducted, with a two tail test was there a significant difference? a. No, because 5.0 is not bigger than 6.89 b. Yes, because 5.0 is bigger than 1.68. c. Yes, because 5.0 is bigger than 1.37 d. Yes, because 5.0 is bigger than 2.02 Let’s try one correct

29. Which is true a. p < 0.05 b. p < 0.01 c. p < 0.001 d. All of the above Let’s try one correct

30. A survey was conducted to see whether women superintendents make more money than men. This is a a. one-tailed test b. two-tailed test c. three-tailed test d. not enough information Note the change in the problem correct

31. A survey was conducted to see whether women superintendents make more money than men. A t-test was conducted, which of the following best describes the results: Note the results were in the unpredicted direction a. reject the null b. do not reject the null c. not enough information Let’s try one correct

32. A survey was conducted to see whether women superintendents make more money than men. A t-test was conducted, which of the following best describes the results: Note the results were in the unpredicted direction a. t(21) = 2.02; p < 0.05 b. t(21) = 2.02; n.s. c. t(37) = 5.0; p < 0.05 d. t(37) = 5.0; n.s Let’s try one correct

34. . Homework

35. .

36. . Type of instruction Exam score 50 0.05 2-tail 2.66 2.02 40 38 p = 0.0113 yes CAUTION This is significant with alpha of 0.05 BUT NOT WITH alpha of 0.01 The average exam score for those with instruction was 50, while the average exam score for those with no instruction was 40. A t-test was conducted and found that instruction significantly improved exam scores, t(38) = 2.66; p < 0.05

37. . Homework Type of Staff Travel Expenses 142.5 0.05 2-tail 1.53679 2.2 130.29 11 p = 0.153 no The average expenses for sales staff is 142.5, while the average expenses for the audit staff was 130.29. A t-test was conducted and no significant difference was found, t(11) = 1.54; n.s.

38. . Homework Location of lot Number of cars 86.24 0.05 2-tail -0.88 2.01 92.04 51 p = 0.38 no The average number of cars in the Ocean Drive Lot was 86.24, while the average number of cars in Rio Rancho Lot was 92.04. A t-test was conducted and no significant difference between the number of cars parked in these two lots, t(51) = -.88; n.s. Fun fact: If the observed t is less than one it will never be significant

39. . Please hand in your homework – they must be stapled

40. Study Type 2: t-test Study Type 3: One-way Analysis of Variance (ANOVA) Comparing more than two means We are looking to compare two means

41. Single Independent Variable comparing more than two groups Study Type 3: One-way ANOVA Single Dependent Variable (numerical/continuous) Independent Variable: Type of incentive Levels of Independent Variable: None, Bike, Trip to Hawaii Dependent Variable: Number of cookies sold Levels of Dependent Variable: 1, 2, 3 up to max sold Between participant design Causal relationship: Incentive had an effect – it increased sales Ian was interested in the effect of incentives for girl scouts on the number of cookies sold. He randomly assigned girl scouts into one of three groups. The three groups were given one of three incentives and looked to see who sold more cookies. The 3 incentives were 1) Trip to Hawaii, 2) New Bike or 3) Nothing. This is an example of a true experiment Used to test the effect of the IV on the DV How could we make this a quasi-experiment?

42. Single Independent Variable comparing more than two groups Study Type 3: One-way ANOVA Single Dependent Variable (numerical/continuous) Ian was interested in the effect of incentives for girl scouts on the number of cookies sold. He randomly assigned girl scouts into one of three groups. The three groups were given one of three incentives and looked to see who sold more cookies. The 3 incentives were 1) Trip to Hawaii, 2) New Bike or 3) Nothing. This is an example of a true experiment Used to test the effect of the IV on the DV None New Bike Sales per Girl scout Trip Hawaii None New Bike Trip Hawaii Dependent variable is always quantitative In an ANOVA, independent variable is qualitative (& more than two groups) Sales per Girl scout