Psych Stats Quiz 5/8/23

Question 1

rank sum test

Answer 1

nonparametric equivalent of the independent t

Question 2

why use rank sum

Answer 2

when you want to compare two samples of ordinal data because the assumption of interval/ratio is not met

Question 3

disadvantages of rank sum

Answer 3

less powerful than parametric tests - type II error is more likely
null hypothesis is more general

Question 4

t-test for dependent samples

Answer 4

looking for differences within subjects
structurally similar to other t statistics but based on difference score (D)

Question 5

t-test for dependent samples: advantages

Answer 5

• use same subjects in all treatment conditions - no risk that subjects in one condition are substantially different from subjects in another
• only need half the subjects for the same n
• more powerful than independent sample t-test because its influenced by variability that is reduced by using the same subjects

Question 6

t-test for dependent samples: within-subjects design

Answer 6

each subject contributes a score to each sample/condition in an experiment

Question 7

t-test for dependent samples: matched-group design

Answer 7

pairs of subjects who are similar - one member of the pair is assigned to one sample and the other is assigned to the second sample
- each subject contributes one score

Question 8

steps for dependent samples

Answer 8

1) state hypotheses (always = 0 and not = 0)
2) Find critical region: df = n-1 and then use t table to find value
3) Difference scores: after score - before score
4) sample mean: average D values
5) sum of squares = sum of each score square - (sum of scores)squared divided by n
6) standard deviation: square root of SS/n-1
7) standard error: sd/square root of n
8) t = average of D/standard error
9) fail to reject the null if t falls within critical region

Question 9

steps for dependent samples

Answer 9

1) state hypotheses (always = 0 and not = 0)
2) Find critical region: df = n-1 and then use t table to find value
3) Difference scores: after score - before score
4) sample mean: average D values
5) sum of squares = sum of each score square - (sum of scores)squared divided by n
6) standard deviation: square root of SS/n-1
7) standard error: sd/square root of n
8) t = average of D/standard error
9) fail to reject the null if t falls within critical region

Question 10

naturally occurring population

Answer 10

present without any intervention by the investigator

Question 11

hypothetical population

Answer 11

does not exist until they are actually measured

Question 12

confound

Answer 12

systematic differences between two groups OTHER than the independent variable
arises due to differences between the groups that existed before the independent variable was applied