Parametric vs. Non-Parametric tests

Question 1

What are the degrees of freedom for one sample t-tests?

Answer 1

N-1

Question 2

What are the degrees of freedom for paired t-tests?

Answer 2

N-1

Question 3

What are the degrees of freedom for independent groups t-tests?

Answer 3

(Na - 1) + (Nb - 1)

or

(Na + Nb) - 2

Question 4

What are the degrees of freedom for pearson’s r test for correlation?

Answer 4

N - 2

Question 5

What are the degrees of freedom for z tests?

Answer 5

0

Question 6

What N do one sample t-tests depend on?

Answer 6

N-1

Question 7

What N do paired t-tests depend on?

Answer 7

N-1

Question 8

What N do independent groups t-tests depend on?

Answer 8

(Na - 1) + (Nb - 1)

or

(Na + Nb) - 2

Question 9

What N do pearson’s r tests depend on?

Answer 9

N*

Question 10

What N do z tests depend on?

Answer 10

Does not depend on N

Question 11

For all tests but the z-test (i.e. one sample t-test, paired t-test, independent groups t-test and pearson’s r) we need to look at a different row (distribution) depending on …..?

Answer 11

The sample size

Question 12

What is the degree of freedom?

Answer 12

It is related to the sample size and tells you which distribution you need to use

It also relates to how much data/information you have, and therefore how good your sample statistics are likely to be (because the bigger the sample size, the better estimate a sample mean is of a population mean)

Question 13

These tests make certain important assumptions about populations from which data are sampled

What are they?

Answer 13

Parametric tests

Question 14

What are parametric tests?

Answer 14

Tests that make certain important assumptions about populations from which data are sampled

Question 15

What are non-parametric tests?

Answer 15

Tests that make far fewer assumptions about populations from which data are sampled

Question 16

These tests make far fewer assumptions about populations from which data are sampled

What are they?

Answer 16

Non-parametric tests

Question 17

Which test (parametric or non-parametric) can be applied more readily and is the “play safe” option?

Answer 17

Non-parametric tests

Question 18

What are the parametric testing common assumptions? List 3

Answer 18

1) Populations from which samples are drawn should be normally distributed

2) Variances (standard deviations) of the populations should be approximately equal

3) No extreme scores (since these have a big impact on the estimated sample statistics)

Question 19

Why use parametric testing if there are so many assumptions?

Answer 19

Because it’s typically more powerful and sensitive than other approaches

Non-parametric approaches are less likely to find more subtle but statistically significant effects in noisy data

Question 20

Why do we use non-parametric tests?

Answer 20

They impose fewer assumptions on underlying data (you can use it more readily); our data does not need to meet all assumptions of the parametric test and can be not-normal

Question 21

What do non-parametric tests do?

Answer 21

They throw away information and the emphasis tends to be on ranks of data rather than actual scores

They are less sensitive to potential stat. effects that are present

Question 22

Why use non-parametric testing if it’s less powerful than parametric?

Answer 22

Because sometimes the assumptions of parametric testing are violated

It is a “play safe” option; it’s for when you don’t have good evidence to support the assumption or you are unsure whether you data are normal or not

Question 23

What is considered the basis of several non-parametric tests?

Answer 23

Ordering and ranking data

Question 24

Relative to parametric tests, non-parametric tests rely upon (……….) assumptions about the distributions from which data are drawn

Answer 24

Fewer

Question 25

Consequently, we would typically use non-parametric tests when it is (……….) to use a parametric alternative because of an assumption violation.

Answer 25

Inappropriate

Question 26

The disadvantage of non-parametric tests is that they are often (…………) that their parametric counterparts due to throwing away metric information about the (………..) and focusing on (………..)

Answer 26

1) Less sensitive
2) Scores
3) Ranks

Question 27

You have 2 samples of data, one from population A and the other from population B. The sample from population A has size nA. The sample from population B has size nB.

What are the degrees of freedom (df) for the following tests based on these samples:

1) A 1-sample t-test to assess whether the mean of the sample from population A is different to some hypothetical value muH

Answer 27

1) Na - 1

Question 28

You have 2 samples of data, one from population A and the other from population B. The sample from population A has size nA. The sample from population B has size nB.

What are the degrees of freedom (df) for the following tests based on these samples:

2) Assuming the samples from populations A&B can be paired and nA = nB = n, a paired t-test is to assess the evidence that the samples came from populations with different means.

Answer 28

2) N - 1

Question 29

You have 2 samples of data, one from population A and the other from population B. The sample from population A has size nA. The sample from population B has size nB.

What are the degrees of freedom (df) for the following tests based on these samples:

3) An independent groups t-test to assess evidence that the samples came from populations with different means

Answer 29

3) (Na - 1) + (Nb - 1)
or
(Na + Nb) - 2

Question 30

You have 2 samples of data, one from population A and the other from population B. The sample from population A has size nA. The sample from population B has size nB.

What are the degrees of freedom (df) for the following tests based on these samples:

4) Assuming the samples from populations A & B represent paired variables for a group of nA = nB = n individuals, a Pearson’s r test of correlation between variables.

Answer 30

4) N - 2