Term
How do independent measures and correlated groups t test differ? 

Definition
Independent Measures:  2 separate samples  presumed to come from 2 separate populations
Correlated Groups  single sample from single population used twice on the same dependent variable  2 samples from same population 


Term
Provide examples for a correlated groups t test. 

Definition
 Before vs. After  Cond. 1 vs. Cond. 2,  Score at Time 1 vs. Score at Time 2 


Term
What advantages are there to running a correlated groups t test? 

Definition
 more variables ascertained and controlled  more realistic design than 1 sample t and z  donâ€™t need parameters like μ and σ  SS can be own control group (in repeated measures) 


Term
What is the difference between repeated measures and matched pairs design? 

Definition
repeated measures:  single sample randomly selected and generates 2 sets of data  don't need a control group because the sample is own control
matched pairs:  2 heavily matched samples taken from single population and measured one time  matched on as many variables as possible 


Term
What are the mechanics of a correlated groups t test? 

Definition
 collect 2 sets of data in Correlated Groups t test  but use only 1 set of data called Difference Scores (D) > all calculations use sample Difference scores in Correlated Groups t test.  makes this a 2 sample t test that looks like a 1 sample t test 


Term

Definition


Term
What is represented by the denominator of this hypothesis test? 

Definition
Smd (standard error of sample means) = estimated standard error for the Difference scores.It is how much we can expect between MD and μD to differ based on chance alone 


Term
The sampling distribution for a correlated groups t test is what? 

Definition
all the possible values of differences you can get between 2 scores on the x axis and the probability of getting those scores (y axis 


Term
Denominator of an independent measures t test is... 

Definition
sM = estimated standard error of the mean (estimated σM) where σ is unknown and we have to rely on the sample s to know how much difference we can expect between M and μ based on chance alone 

