ANOVA Flashcards
types of t-test
independent samples t test
paired samples t test
one-sample t test
independent samples t test
compares means from 2 independent groups
paired sampled t test
compares means from 2 sets of individuals
repeated measures, matched subjects
one sample t test
compares observed mean to population mean
when to use t test over anova
more efficient with 2 groups
when to use anova over t test
more efficient with more than 2 groups
when to use anova
when want to compare more than 2 conditions
have 2 or more groups/conditions and more than one IV/factor
advantages of anova
can investigate effect of multiple factors on DV at same time
why not just use several t tests
this can increase chance of type 1 error - experiment wise/familywise error rate
anova controls for errors so type one errors remain at 5% so you can be confident significant results aren’t down to chance
anova assumptions
DV at interval or ratio level
Data from normally distributed population
Homogeneity of variance
For independent groups design, independent random samples taken from each population
nominal data
e.g. gender
numbers distinguish categories but no ranking
ordinal data
use scale to order/rank
size of number and differences mean nothing
interval data
scores in order, equal differences, no absolute 0
e.g. temperature
ratio data
e.g. height
scores in order, equal differences, absolute 0
check for normally distributed data
histogram
skew and kurtosis in descriptives table
