1. Comparing Two Means: One-sample & Paired-sample t-tests Lesson 13

2. Inferential Statistics n Hypothesis testing l Drawing conclusions about differences between groups l Are differences likely due to chance? n Comparing means l t-test: 2 means l Analysis of variance: 2 or more means ~

3. Comparing 2 means: t-tests n One-sample t-test l Is sample likely from particular population? n Paired-Sample t-test l 2 dependent (related) samples n Independent-samples t-test l 2 unrelated samples ~

4. The One-sample t-test n Evaluating hypothesis about population l taking a single sample l Does it likely come from population? n Test statistics z test if  known t test if  unknown ~

5. t statistic

6. Example: One-sample t-test n Survey: college students study 21 hr/wk l Do Coe students study 21 hrs/week? Select sample (n = 16)  unknown n Nondirectional hypothesis: H 0 :  = 21; H 1 :   21 l reject H 0 if increase or decrease n PASW/SPSS: Test value = 21 l Assumed from H 0 ~

7. PASW One Sample T Test n Menu l Analyze l Compare Means l One-Sample T Test n Dialog box l Test Variable(s) (DV) Test Value (value of  testing against) l Options (to change confidence intervals) ~

8. PASW Output *1-tailed probability: divide Sig. 2-tailed by 2

9. Paired-Samples t-tests n 2 samples are statistically related l Less affected by individual differences l reduces variance due to error n Repeated-measures l 2 measurements on same individual n Matched-subjects l Match pairs on some variable(s) l Split pairs into 2 groups ~

10. Difference Scores n Find difference between each score l D = X 2 - X 1 l Requires n 1 scores equal n 2 scores n Calculate mean D l n And standard deviation of D l ~l ~

11. Repeated-measures n 2 measurements of same individual n Pretest-posttest design l measure each individual twice l pretest  treatment  posttest l compare scores ~

12. Matched-subjects n Match individuals on important characteristic l individuals that are related l IQ, GPA, married, etc n Assign to different treatment groups l each group receives different levels of independent variable ~

13. Assumptions: Related Samples n Population of difference scores is normal n Observations within each treatment independent l scores for each subject in a group is independent of other subjects scores ~

14. Related-samples Hypotheses n Nondirectional H 0 :  D = 0 H 1 :  D  0 n Directional H 0 :  D > 0 H 1 :  D < 0 l Remember: it depends on the direction of the prediction ~

15. Sample Statistics n Mean difference n Mean for single sample

16. Standard Deviation: Related-samples Single sample

17. Estimated Standard Error n Calculate same as single sample l use standard deviation of difference scores

18. Test Statistic n Related-samples t test Since  D = 0

19. Example n Is arachnophobia limited to real spiders or is a picture enough? n Participants l 12 spider phobic individuals n Manipulation (IV) l Each person exposed to a real spider & picture of same spider at two different times n Outcome (DV): Anxiety

20. PASW Paired-Sample T Test n Data entry l 1 column each DV n Menu l Analyze l Compare Means l Paired-Sample T Test n Dialog box l Paired Variable(s) (DV) l Options (to change confidence intervals) ~

21. PASW Output

22. Reporting the Results n On average, participants experienced significantly greater anxiety to real spiders (M = 47.00, SE = 3.18) than to pictures of spiders (M = 40.00, SE = 2.68), t(11) = −2.47, p <.05