2. Introduction to Statistics for the Social Sciences SBS200, COMM200, GEOG200, PA200, POL200, or SOC200 Lecture Section 001, Spring 2015 Room 150 Harvill Building 8:00 - 8:50 Mondays, Wednesdays & Fridays.

4. Schedule of readings Before next exam (April 10 th ) 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

5. By the end of lecture today 3/11/15 Use this as your study guide Confidence Intervals Project 2 – t-tests

6. Labs continue this week Project 2

7. 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

8. . Type I or type II error? Who is taller men or 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 This is an example of a _____. a. correlation b. t-test c. one-way ANOVA d. two-way ANOVA t-test Between Or within? Quasi or True? Between Quasi

12. . Type I or type II error? Curly versus straight hair – which is more “dateable”? This is an example of a _____. a. correlation b. t-test c. one-way ANOVA d. two-way ANOVA t-test Between Or within? Between Independent Variable? Dependent Variable? Type of Hair Dateability

13. 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 What were the dependent variables used? Which comparison uses chi-square?

14. Have a safe and happy spring break No class on Friday. Lab homework only: Work on gathering data for Project 2 in Labs

15. We now know all components of actually calculating confidence intervals: When to use confidence intervals: when you are estimating (guessing) a single number by providing likely range that the number appears in How to calculate confidence intervals Simply finding the raw score that is a certain distance from the mean that is associated with an area under the curve The relevance of the Central Limit Theorem When we are predicting a value we will use the standard error of the mean (rather than the standard deviation) Standard Error of the Mean (SEM) Review

16. Confidence Intervals (based on z): We are using this to estimate a value such as a population mean, with a known degree of certainty with a range of values The interval refers to possible values of the population mean. We can be reasonably confident that the population mean falls in this range (90%, 95%, or 99% confident) In the long run, series of intervals, like the one we figured out will describe the population mean about 95% of the time. Can actually generate CI for any confidence level you want – these are just the most common Standard Error of the Mean (SEM) Greater confidence implies loss of precision. (95% confidence is most often used) Subjective vs Empirical

17. . Please find the raw scores that border the middle 99% of the curve Please find the raw scores that border the middle 95% of the curve 95% Confidence Interval: We can be 95% confident that the estimated score really does fall between these two scores 99% Confidence Interval: We can be 99% confident that the estimated score really does fall between these two scores

18. Confidence Intervals (based on z): A range of values that, with a known degree of certainty, includes an unknown population characteristic, such as a population mean How can we make our confidence interval smaller? Decrease Variability Increase sample size (This will decrease variability) Decrease level of confidence Decrease variability through more careful assessment and measurement practices (minimize noise). 95%

19. Confidence interval uses SEM

20. Upper boundary raw score x = mean + (z)(standard deviation) x = 55 + (+ 2.58)(10) x = 80.8 Lower boundary raw score x = mean + (z)(standard deviation) x = 55 + (- 2.58)(10) x = 29.2 29.2 80.8 29.2 80.8

21. Upper boundary raw score x = mean + (z)(standard error mean) x = 55 + (+ 2.58)(1.42) x = 58.7 Lower boundary raw score x = mean + (z)(standard error mean) x = 55 + (- 2.58)(1.42) x = 51.3 29.2 80.8 51.3 58.7 10 49 1.42 51.3 58.7

22. 29.2 80.8 58.7 8.02 8.6 9.18 7.8 8.6 9.4 51.3 10.2 29.8 23.1 16.9 4.09 13.11 9.18 8.02 2.67 14.5 9.4 7.8

24. Have a safe and happy spring break No class on Friday. Lab homework only: Work on gathering data for Project 2 in Labs