power analysis

Question 1

the significance criteria

Answer 1

alpha (risk of making a type 1 error)

usually set at .05

Question 2

type 1 (alpha) error

Answer 2

when a difference or relationship is accepted when there isn’t one

Question 3

type 2 (beta) error

Answer 3

when conclude there is no difference or relationship when there is one

Question 4

effect size

Answer 4

aims to estimate the magnitude of an effect /population parameter (i.e. a difference or a relationship) independent of sample size

Question 5

limitations of statistical significance testing

Answer 5

statistical probability cannot be used as a measure of magnitude because the significance level may be due to sample size or the effect size

Question 6

cohens d guidelines for d (small, medium and large)

Answer 6

.20
.50
.80

Question 7

cohen’s r guidelines (small, medium and large)

Answer 7

.10
.30
.50

Question 8

cohen’s f2 guidlines (Small medium and large)

Answer 8

.02
.15
.35

Question 9

power calculated based on values from

Answer 9

0-1

where 0.1= 10% chance of finding an effect

Question 10

statisical power

Answer 10

the statistical power of a test is the probability of avoiding making a type 2 error (1-B)

recommended at .80
so the chance of making a type 2 error is 20%

Question 11

ratio of type 1: type 2 errors

Answer 11

1:4

Question 12

statical power depends on

Answer 12

effect size- larger more power
sample size- larger more power
precision of measures- reduce standard errors

Question 13

why is the statistical power greater for a 1 tailed test

Answer 13

because more power is given to detect an effect in one direction by not testing the effect in the other direction

Question 14

why is the statistical power greater for within subjects design

Answer 14

because the error variance ‘noise’ is reduced when using the same participants in both conditions of an experiment

Question 15

effect size (in terms of power)

Answer 15

large effects are easier to detect and also less likely to make a type 2 error
if there is a big difference don’t need many people to detect it

Question 16

sample size (in terms of power)

Answer 16

larger sample size gives a more accurate estimate of the means - smaller error bars (SD/ square root of n)
so easier to see a difference between two conditions

Question 17

in the t test- how sample size affects power

Answer 17

error bars represent variability of the mean (i.e. the standard error of the mean)
larger samples result in smaller standard errors
therefore larger sample size the greater the power (due to smaller standard errors) so effects are easier to detect.

Question 18

precision of measure (power)

Answer 18

more reliable measures provide more precise estimates of the latent variable therefore present less error variance/noise
and also smaller standard errors
better estimate of means=less error variance and therefore better chance of finding a true effect if there exists one

Question 19

prospective power analysis called

Answer 19

‘a priori’

Question 20

prospective power analysis

Answer 20

set sig criterion .05
set stat power .80
estimate your effect size as being small medium or large -cohens table (either based on previous research e.g a meta analysis, or from a pilot study)

the power analysis tells you how many ppt you need

Question 21

retrospective ‘sensivity’

Answer 21

set sig criterion .05
set stat power .80
sample size is known
estimate minimum effect size that could have been calculated

(interesting to know why got non sig result-
given this design with this many ppt what could have been detected)

Question 22

retrospective ‘post hoc’

Answer 22

calculating the statistical power - i.e. the likelihood of making a type 2 error

with the sig at .05
and sample size of x
what is the power of the test

is it underpowered?

Question 23

retrospective ‘a priori’

Answer 23

set sig criterion .05
set stat power .80
estimate the required sample size for the effect size

e.g. if got nn significant result- can see how many more ppts would need

Question 24

a priori

Answer 24

to do with sample size

Question 25

sensitivity

Answer 25

what effect size could have been obtained

Question 26

post hoc

Answer 26

testing power type 2 error