2. Lecturer’s desk INTEGRATED LEARNING CENTER ILC 120 Screen 11 10 2 1 98 7 6 5 13 12 15 14 17 16 19 18 4 3 Row A Row B Row C Row D Row E Row F Row G Row H Row I Row J Row K Row L Computer Storage Cabinet Cabinet Table 20 11 10 2 1 9 8 7 6 5 21 20 23 22 25 24 27 26 4 3 28 13 12 14 16 15 17 18 19 11 10 2 1 9 8 7 6 5 21 20 23 22 25 24 26 4 3 13 12 14 16 15 17 18 19 11 10 2 1 9 8 7 6 5 21 20 23 22 25 24 26 4 3 13 12 14 16 15 17 18 19 11 10 2 1 9 8 7 6 5 21 20 23 22 25 24 27 26 4 3 28 13 12 14 16 15 17 18 19 29 11 10 2 1 9 8 7 6 5 21 20 23 22 25 24 27 26 4 3 28 13 12 14 16 15 17 18 19 11 10 2 1 9 8 7 6 5 21 20 23 22 25 24 27 26 4 3 13 12 14 16 15 17 18 19 11 10 2 1 9 8 7 6 5 21 20 23 22 25 24 26 4 3 13 12 14 16 15 17 18 19 11 10 2 1 9 8 7 6 5 21 20 23 22 25 24 4 3 13 12 14 16 15 17 18 19 11 10 2 1 9 8 7 6 5 21 20 23 22 24 4 3 13 12 14 16 15 17 18 19 11 10 2 1 9 8 7 6 5 21 20 23 22 4 3 13 12 14 16 15 17 18 19 11 10 9 8 7 6 5 4 3 13 12 14 16 15 17 18 19 broken desk

3. Introduction to Statistics for the Social Sciences SBS200, COMM200, GEOG200, PA200, POL200, or SOC200 Lecture Section 001, Fall, 2014 Room 120 Integrated Learning Center (ILC) 10:00 - 10:50 Mondays, Wednesdays & Fridays. http://www.youtube.com/watch?v=oSQJP40PcGI

4. Reminder A note on doodling

5. Schedule of readings Before next exam (November 21 st ) Please read chapters 7 – 11 in Ha & Ha Please read Chapters 2, 3, and 4 in Plous Chapter 2: Cognitive Dissonance Chapter 3: Memory and Hindsight Bias Chapter 4: Context Dependence

6. By the end of lecture today 10/27/2014 Use this as your study guide 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?

7. Homework due Assignment 16 One-sample z and t hypothesis tests Due: Wednesday, October 29 th

8. Labs continue this week with Project 2

12. Create example of t-test Identify single IV (two levels) Identify DV (must be numeric) Graph should have two bars (one for each mean) Think about how you might Study Type 2: t-test Comparing Two Means? Use a t-test

13. Five steps to hypothesis testing Step 1: Identify the research problem (hypothesis) Describe the null and alternative hypotheses Step 2: Decision rule Alpha level? ( α =.05 or.01)? Step 3: Calculations Step 4: Make decision whether or not to reject null hypothesis If observed z (or t) is bigger then critical z (or t) then reject null Step 5: Conclusion - tie findings back in to research problem One or two tailed test? Balance between Type I versus Type II error Critical statistic (e.g. z or t or F or r) value?

14. . Type I or type II error? Who is taller men or women? What would null hypothesis be? No difference in the height between men and women Independent Variable? Dependent Variable? IV: Nominal Ordinal Interval or Ratio? DV: Nominal Ordinal Interval or Ratio? IV: Continuous or discrete? DV: Continuous or discrete? Gender Height IV: Nominal DV: Ratio IV: Discrete DV: Continuous

15. . Type I or type II error? Who is taller men or women? What would null hypothesis be? No difference in the height between men and women One-tailed Or Two –tailed? Between Or within? Quasi or True? Two –tailed Between Quasi

16. . Type I or type II error? Who is taller men or women? Type I error: Rejecting a true null hypothesis Saying that there is a difference in height when in fact there is not (false alarm) Type II error: Not rejecting a false null hypothesis Saying there is no difference in height when in fact there is a difference (miss) What would null hypothesis be? No difference in the height between men and women This is an example of a _____. a. correlation b. t-test c. one-way ANOVA d. two-way ANOVA t-test Type I Error Type II Error

17. . Type I or type II error? Curly versus straight hair – which is more “dateable”? Type I error: Rejecting a true null hypothesis Saying that there is a difference in dateability when in fact there is not (false alarm) Type II error: Not rejecting a false null hypothesis Saying there is no difference in dateability when in fact there is a difference (miss) What would null hypothesis be? No difference in the dateability between curly and straight hair This is an example of a _____. a. correlation b. t-test c. one-way ANOVA d. two-way ANOVA t-test

18. Writing Assignment Please watch this video describing a series of t-tests What is the independent variable? How many different dependent variables did they use? (They would conduct a different t-test for every dependent variable) http://www.youtube.com/watch?v=z7kfiA2SXMY http://www.youtube.com/watch?v=n4WQhJHGQB4 http://www.everydayresearchmethods.com/2011/09/curly-or-straight-.html

19. Writing Assignment Worksheet Design two t-tests http://www.youtube.com/watch?v=z7kfiA2SXMY http://www.youtube.com/watch?v=n4WQhJHGQB4

20. Five steps to hypothesis testing Step 1: Identify the research problem (hypothesis) Describe the null and alternative hypotheses Step 2: Decision rule Alpha level? ( α =.05 or.01)? Step 3: Calculations Step 4: Make decision whether or not to reject null hypothesis If observed z (or t) is bigger then critical z (or t) then reject null Step 5: Conclusion - tie findings back in to research problem Critical statistic (e.g. z or t) value? How is a t score same as a z score? Population versus sample standard deviation How is a t score different than a z score?

21. .. 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 or “effect size”) Denominator is always measure of variability (how wide or much overlap there is between distributions) Variability of curve(s) (within group variability)

22. . A note on variability versus effect size Difference between means Variability of curve(s) Variability of curve(s) (within group variability) Difference between means

23. .. Variability of curve(s) Variability of curve(s) (within group variability) Difference between means A note on variability versus effect size

24. . Effect size is considered relative to variability of distributions 1. Larger variance harder to find significant difference Treatment Effect Treatment Effect 2. Smaller variance easier to find significant difference x x

25. . Effect size is considered relative to variability of distributions Treatment Effect Treatment Effect x x Variability of curve(s) (within group variability) Difference between means