1. Chapter 1: Science of Psychology Daily Objective (concept map): Apply basic statistical concepts to explain research findings: - Descriptive Statistics: Central Tendency (mean, median, mode, skewed distributions) Variance ( range, standard deviation, and normal distributions) - Inferential Statistics: Statistical significance (t- test and p-value)

2. The Science of Psychology Approaches to Psych Growth of Psych Research Methods Statistics DescriptiveCorrelationExperiment Case Study Survey Naturalistic Observation DescriptiveInferential Ethics Sampling Central Tendency Variance Careers We are here

3. Outline Descriptive Statistics: –Central Tendency Mean, median, and mode skewed distributions –Variance Range standard deviation normal distributions Inferential Statistics: –Statistical significance t-test and the p-valuet-testp-value –Confidence intervalsConfidence intervals

4. Central Tendency Tendency of scores to congregate around some middle variable A measure of central tendency identifies what is average or typical in a data set

5. Measures of Central Tendency Mode: The most frequently occurring score in a distribution. Mean: The arithmetic average of scores in a distribution obtained by adding the scores and then dividing by their number. Median: The middle score in a rank- ordered distribution.

6. But the mean isn’t as accurate in a skewed distributionmean The Median is a much better measure of the center

7. Positively Skewed Negatively Skewed Skewed distributions

8. Measures of Variation Statistical dispersion (how distributed the data points are) is a key concept in statistics. Two key ways of measuring statistical dispersion » Range » Standard Deviation

9. Range The range simply gives the lowest and highest values of a data set.

10. Standard Deviation Standard deviation gives a measure of dispersion. Essentially, standard deviation measures the average difference between the values/numbers/scores Standard deviation and mean give the researcher a lot of information about the collected data

11. Formulas for Standard Deviation

12. Standard Deviation

13. Standard Deviation in Action A couple needs to be within one standard deviation of each other in intelligence (10 points in either direction). —Neil Clark Warren, founder of eHarmony.com

14. Normal Distributions The distribution of data also gives us key info. We know that many human attributes… e.g height, weight, task skill, reaction time, anxiousness, personality characteristics, attitudes etc. …follow a normal distribution.

15. Normal Distribution

16. IQ follows a Normal Distribution Mean = 100 SD = 15

17. What percentage score below 100? Mean = 100 SD = 15

18. What percentage score below 100? Mean = 100 SD = 15

19. What percentage score above 100? Mean = 100 SD = 15 34.1% + 13.6% + 2.1%

20. Normal Distribution

21. What percentage score between 85 and 100? Mean = 100 SD = 15 34.1%

22. Normal Distribution

23. What percentage score between 85 and 115? Mean = 100 SD = 15 34.1% + 34.1% = 68.2%

24. What percentage score between 70 and 130? Mean = 100 SD = 15 13.6% + 34.1% + 34.1% + 13.6% = 95.4%

25. What percentage score below 70 and above 130? Mean = 100 SD = 15

26. Inferential Statistics You are trying to reach conclusions that extend beyond just describing the data. These are used to test hypothesis about samples. Outline

27. Testing for Differences If we have results (means) from two groups, before we infer causation we must ask the question: Is there a real difference between the means of the two groups or did it just happen by chance? To answer the question, we must run a t-Test

28. Example of when to do a t-test Does caffeine improve our reaction time? We recruit 40 people and give (random assignment) » 20 a caffeine pill (experimental group) » 20 a sugar pill (control group) We give them a brief reaction time test and record the results.

29. Experimental Group results (caffeine) » Mean = 500.32ms » SD = 172.60ms Control Group results (placebo) » Mean = 608.64ms » SD = 146.93 Example of when to do a t-test

30. CaffeineNo Caffeine Example of when to do a t-test

31. Why can’t I be done! Yes, they are different... But you don’t know if that difference was due to your IV (caffeine) or just dumb luck. You have to be sure that the results are statistically significant

32. T-Test formula

33. T-test excel formula =TTEST(array1,array2,tails,type) Array1 is the first data set. Array2 is the second data set. Tails specifies the number of distribution tails. If tails = 1, TTEST uses the one-tailed distribution. If tails = 2, TTEST uses the two-tailed distribution. Type is the kind of t-Test to perform. IF TYPE EQUALSTHIS TEST IS PERFORMED 1Paired 2Two-sample equal variance (homoscedastic) 3Two-sample unequal variance (heteroscedastic)

34. T-test yields a p-value Generally, the t test gives a P value that allows us a measure of confidence in the observed difference. It allows us to say that the difference is real and not just by chance. A p value of less than 0.05 is a common criteria for significance. We call this statistically significant

35. T-test results Does caffeine improve our reaction time? Caffeine condition has a lower mean RT. We run a t-test on our samples and get: » p = 0.039 Can we be confident that the difference in the data is not due to chance? roups, an ANOVA tests the difference between the means of two or more groups.