Chapter 16 Flashcards

(8 cards)

1
Q

random sampling distribution of r

A
  • Not normally distributed
    • If rho = 0, it’s symmetrical and close to normal
    • If rho =/= 0, it’s skewed because rho can’t exceed +/- 1 -> larger the absolute value of rho, greater skew
  • Smaller n = greater skew
  • Values of r will vary less from sample to sample when n is large
  • Values of r will vary less from sample to sample when rho is strong
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

when is the random sampling distribution of r skewed?

A
  • If rho =/= 0, it’s skewed because rho can’t exceed +/- 1 -> larger the absolute value of rho, greater skew
  • Smaller n = greater skew
  • If rho = 0, it’s symmetrical and close to normal
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

rho

A
  • Pearson’s correlation coefficient for population of paired scores
  • Only absolute value matters (it gets squared, no +/- not important)
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

desirable properties of fisher’s z transformation

A
  • Sampling distribution is normal (regardless of rho or n)
  • Standard error of z’ is independent of rho values
  • Non-linear transformation that will change the shape of the distribution (and will normalize it)
  • Magnitude: if r is negative, zF is also negative (don’t forget to put +/- in your answer!)
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

What can you do with perfect correlations? Why can you do it?

A

If you have perfect correlations, you can create a formula to convert one value to another (because there’s no variability)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

why do we prefer the t-test for correlations?

A
  • T-test is better than z-test because it’s more powerful for correlations (so we don’t use the z-test)
  • T-test gains its power by using sample estimate of r to estimate standard error
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

H0 for testing correlations

A
  • that they’re 0 -> any significant correlation means that there’s little chance that the correlation is 0
  • Remember that H0 can never be true, just a plausible option
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

1-tail vs. 2-tail tests

A
  • 2-tail: detects significant correlations in either the positive or negative direction (if H0 is rejected, we can conclude either that correlation is > or < 0)
  • 1-tail: if it’s logical that the correlation won’t be 0 but can’t be negative (or vice versa), can use a 1-tail test (more power -> critical region concentrated in one tail)
How well did you know this?
1
Not at all
2
3
4
5
Perfectly