1. Last Time Today we will learn how to do t-tests using excel, and we will review for Monday’s exam

2. Using Excel to compute t-tests =ttest(array1,array2,tails,type) Select the arrays that you want to compare, specify number of tails (1 or 2) and type of t-test (1=dependent, 2=independent w/equal variance assumed, 3=independent w/unequal variance assumed). Returns the p-value associated with the t- test.

3. Practice problem You are conducting an experiment about methods for teaching reading. You randomly assign five students to an experimental reading intervention, and another five to receive instruction as usual in their classrooms. After the intervention, you measure the number of words each child can read correctly in one minute, and obtain the following results. Group 1 (experimental): 30, 35, 40, 20, 32 Group 2 (usual instruction): 25, 30, 20, 18, 18 Use Excel to conduct a t-test to find out whether the groups are different in reading ability at the end of the study.

4. Practice problem Now assume the scores represent pretest and posttest scores for one group, before and after they received the reading intervention. Posttest scores: 30, 35, 40, 20, 32 Pretest scores: 25, 30, 20, 18, 18 Use Excel to conduct a t-test to find out whether the the children’s reading ability improved between the pretest and posttest.

5. Exam II (Monday 10/8) Covers first 11 chapters (emphasis on chapters 9-11) Closed book (materials will be provided) Bring calculator Bring pencil Emphasis on material covered in class Difficulty level similar to Exam I, but much more material to know Some SPSS and Excel may be required

6. Old Material Terms from chapter 1 Ways to describe distributions (using tables, graphs, numeric summaries of center and spread) Z-scores Probability Characteristics of the normal distribution Use of the unit normal table Central Limit Theorem & distribution of sample means Standard error of the mean ( σ M ) Hypothesis testing (four steps) Effect sizes and power Type I and Type II error

7. New Material: t-tests Similarities/differences among four inferential tests: One-sample Z test One-sample t-test Independent samples t-test Paired samples t-test When to use which test Formulae for each statistic Numerator (difference between means) Denominator (standard error) Degrees of freedom Use of the t-table with various alpha levels and one-tailed and two-tailed scenarios Effect sizes Estimation and confidence intervals

8. New Material: t-tests Assumptions of the t-tests Levene’s test for equality of variance Using SPSS/Excel to conduct t-tests Setting up the data properly Knowing which commands to use Interpreting the output