9 terms

Psych 12 Final (One Factor Designs)

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
-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