Hypothesis Tests: Categories Flashcards
summarize which test to use, given the predictor data type and outcome data type
describe chi-squared tests
- chi-squared tests are for categorical outcomes and predictors, when you have no information on variance on the predictor (chi-tegorical)
- the null hypothesis is that the predictor varies randomly by category of the outcome
- the alternative hypothesis is that the predictor differs nonrandomly by category of the outcome
describe the equation for Chi-squared
describe T-tests
- T-tests are for comparing the means of two groups
- (Tea is for Two)
- the null hypothesis is that the 2 groups have the same mean value of the predictor
- the alternative hypothesis is that the 2 groups have different mean values of the predictor
explain how a T-test works
a T-test answers the question, “how many standard error apart are these 2 sample means?”
- if many, the true means are probably different; reject the null hypothesis
- if not many, the true means may be the same; accept the null hypothesis
the p-value tells, “how likely is it that these 2 sample means would be this far apart, if they are in fact drawn from the same population?”
describe details and assumptions of T-tests
- T-tests compare population means (not any other measure of central tendency); they assume the sample population is normally distributed
describe ANOVA
T-test for 3 or more groups
- ANOVA partitions the total variance into within-group and between-group
- the null hypothesis: the means of all 3 categories are not significantly different (that all variance is within-group)
- the alternative hypothesis: at least 1 category is different (that at least some variance is between-groups)
name 1 way to decrease within-group variation
one way to decrease within-group variation is with a paired test
- paired tests measure the same subjects with and without the predictor of interest
- this decreases within-group variation, since all within-subjects traits are corrected for
list the 3 ways to increase statistical power
- increase sample size
- decrease within-group variance
- sample only the most extreme individuals (the “best examples” of each group)
summarize the types of tests, in increasing order of power