Psych 12 Final (One Factor Designs)
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9 terms
Terms | Definitions |
|---|---|
 Inferential statistics | How do we know whether group differences are big enough? Used to test hypothesis about data and to draw conclusions about reliability and generalizability of findings (t-tests, ANOVAs, chi-square) |
| Scores differ for two reasons | -Variation between groups (good) -Variation within groups (bad) -We want big variation between groups relative to variation within groups |
| How inferential statistics work | -estimate variation within groups and between groups -estimate how much the groups differ by chance alone (use critical values) -determine whether your group differences are larger than the difference expected by chance alone |
| P-values | -an estimate of the probability that your effect (group differences) is due to chance alone -arbitrary cut-off 0.5 (5% probability is due to chance, that is a fluke) |
| T-tests for 2-group designs: statistical difference: sample size | -typically for experiments & quasi-experiments -compares the mean difference between 2 groups to variation within each group |
| Effect size measures | -(cohen's d, eta-squared) -important in eliminating the influence of sample size |
| Designs with 3 or more groups (1 IV) | -Type 1 error -alpha level (5% probability of type 1 error) -the chance the test will show an effect when none exists -Type 2 error -beta level (20% probability of type 2 error) -the chance the test will not show an effect when one does exist |
| Alpha inflation | as we conduct more and more statistical tests, the chance of a Type 1 error increases across the entire collection of tests |
| One-way ANOVA | -omnibus tests - checks to see if there is any difference between groups -F statistic - life the t-test -if one-way ANOVA is significant |
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