Chapter 11: Dependent Samples T-Tests Flashcards
If the confidence interval contains 0
then it is not a significant effect
If the interval doesn’t contain 0
then the treatment effect was significant
assumptions for an independent t-test
- Observations from each sample are independent
- The two populations the samples come from must have a normally distributed dependent variable
- The two populations the samples come from must have equal variances of the dependent variable
Called the homogeneity of variance assumption
homogeneity of variance assumption
The two populations the samples come from must have equal variances of the dependent variable
what is the goal of hartley’s f max test?
Test for homogeneity of variance assumption
interpreting hartley’s f max values
Small value (near 1.00) indicates similar sample variances
f-max formula
f-max= larger variance/ smaller variance
what information do you need to calculate f-max?
Alpha level
K (how many samples you have)
Df for each sample variance
what if you don’t meet the homogeneity of variance assumption?
you can use a different method to compute the variance and associated standard error (which a computer will do for you!)
when to use dependent t-tests?
Used when you have two scores for one participant
matching-subjects design
Each individual in one treatment is matched one-to-one with a corresponding individual in the second treatment
difference score
D= X₂- X₁
where X₁ is the person’s score in the first treatment, X₂ is the person’s score in the second treatment
null hypothesis for a dependent t-test
H₀: μ= 0
States that the population of difference scores has a mean of zero
alternative hypothesis for a dependent t-test
H₁: μ≠ 0
States that there is a systematic difference between treatments that causes the difference scores to be consistently positive (or negative) and produces a non-zero mean difference between the treatments
df for dependent t-test
n-1