Experimental Psychology Arlo Clark Foos

Transcription

1 Inferential Statistics Experimental Psychology Arlo Clark Foos

2 Descriptive vs. Inferential Stats Descriptive Inferential

3 Inferential Statistics Did your IV have an effect? By chance alone Large vs. Small values

4 Null vs. Alternative Hypotheses H o = Null Hypothesis Any differences between groups are due to chance alone H a (or H 1, H 2, etc.) = Alternative (Experimental Hypothesis Any differences between groups are due to the IV

5 By chance alone When is something rare enough? 30 times in every 100? 5 times in every 100? Significance level (α)=.30 Significance level (α)=.05

6 Differences between groups Independent Samples t testtest Independence? Is there another type?

7 Detroit Tigers example M = M = t = 2.61

8 Sampling Distributions Critical Values

9 Number of Participants Relationship between number of participants and variability Effect on sampling distribution (t distribution) What does this mean about conducting a t test? test?

10 Degrees of Freedom In the context of making an estimate (e.g., Mean) df = # of scores that are free to take on any value Examples Back to the Tigers

11 One Tail or Two Tail Test? Directional vs. Nondirectional Hypotheses Which test makes it easier to reject the null? Valid?

12 Student s t distribution Calculated t value vs. Critical t values

13 Back to our Tigers example 1984: M = : M = df = (N ) + (N ) df = 14 t(14) =

14 Statistical Errors Recall assumptions of significance (α) level 5 out of every 100 replications Implications?

15 New Fad: Effect Size Statistical Significance vs. Effect Size Effect Size not affected by sample size

16 Paired Samples t test test Paired samples, Repeated measures,within Subjects, Dependent Refers to a contrast between groups of participants who were assigned to groups through matched pairs, natural pairs, orrepeated repeated measures. We are essentially comparing scores within the same participants (subjects). Test Retest, Time1 Time2, Trial Types

17 Paired Samples t test test Advantages of correlated groups designs Control issues The three methods for creating correlated groups designs give us greater certainty of group equality. Statistical issues Correlated groups designs can help reduce error variation. Error variability Variability in DV scores that is due to factors other than the IV individual differences, measurement error, and extraneous variation (also known as within groups variability).

18 Independent Samples t testtest Advantages of independent groups designs Simplicity Use of correlated groups designs is impossible in some situations.

19 Interpreting Your Stats Thet test t test for independent samples Translating statistics into words If two equal groups began the experiment and they are now unequal, to what can we attribute the difference? If our controls have been adequate, our only choice is to assume that the difference between the groups is due to the IV. For example, if you were writing an interpretation of the results from the sample experiment in your text, you might write something like the following: Salesclerks who waited on well dressed customers (M = 43.38, SD = 10.11) took significantly less time, t(14) = 2.61, p =.021, to respond to customers than salespeople who waited on customers dressed in sloppy clothing (M = 63.25, SD= = 12.54). The effect size, estimated with Cohen s d, was.92.

20 Interpreting Your Stats The t test test for correlated samples Translating statistics into words Example from the text: Salespeople who waited on well dressed customers (M = 48.38, SD = 10.11) took significantly less time, t(7) = 5.47, p =.001, to respond to the customers than when they waited on customers dressed in sloppy clothes (M = 63.25, SD = 12.54). The effect size, estimated with Cohen s d, was 1.93.