Hypothesis testing: L3-4

Question 1

why use statistical tests?

Answer 1

how likely it is that we got our results by chance or if the results represent a real difference

Question 2

• why use the t-distribution ?

- what does it take into account?

Answer 2

• we don’t know the population SD

- expected mean, measure of the standard error of the mean based on the sample

Question 3

what do the DF change?

Answer 3

shape of the distribution
-> looks broader for lower df
-> normal distribution for large df
= cut-off varies depending on df

Question 4

experimental design: one-sample design

1. what is it
2. advantages
3. disadvantages

Answer 4

1. one group values coming from different people
2. used to compare group data to known values
3. • may not know population values
     - may want to compare 2 groups to investigate change of behaviour over time

Question 5

experimental design: between-groups/ independent-measures design

1. what is it
2. advantages
3. disadvantages

Answer 5

1. 2 groups, values come from different people
2. • measurements are independent
     - don’t have to worry about learning effects due to repeat exposure
3. • people in different groups might be v different -> need large sample sizes or counterbalance all factors that might influence results
     - cannot study behaviour over time

Question 6

experimental design: within-groups/ repeated-measure design

1. what is it
2. advantages
3. disadvantages

Answer 6

1. single group providing data for both conditions
2. • no differences b/groups
     - study changes in behaviour over time
     - test less people
3. • measurements not independent -> need to calculate variance differently
     - repeated exposure

Question 7

what do we assume about Ho?

Answer 7

it is true until we are sufficiently convinced that our result is “very unlikely” under this hypothesis

Question 8

how to calculate est. standard error of the mean

Answer 8

standard deviation
_____________

sample size square root

Question 9

• how to get standard deviation?

- how to get SS?

Answer 9

• take the square root of the variance

- all raw values - M squared and added together

Question 10

what do we find to reject Ho?

Answer 10

t empirical > t critical

Question 11

any value > 2.353 is what?

Answer 11

less likely than 5% to occur by chance

Question 12

what would a non-directional hypothesis look like?

Answer 12

H1 = Our sample is drawn from a population μ>10 OR μ<10

some difference

Question 13

For non-directional hypothesis what type of tests are used?

Answer 13

two-tailed test

-> .025 either end of distribution

Question 14

what would a directional hypothesis look like?

Answer 14

H1 = Our sample is drawn from a population μ>10

higher/lower proportion

Question 15

For directional hypothesis what type of tests are used?

Answer 15

one-tailed test

-> .05 one end of distribution

Question 16

what can an independent-measures t-test do?

Answer 16

see whether 2 groups, exposed to different experimental conditions differ in a particular measure

Question 17

independent-measures t-test formula

Answer 17

same as general structure, but now tests whether a difference between 2 means is significantly different from chance

Question 18

What is the complication of this test & how is it calculated?

Answer 18

2 variances considered when calculating standard error of mean
-> pooled variance

Question 19

how is pooled variance calculated?

Answer 19

summing up the summed squared differences (SS) from each condition & dividing by the sum of the df
= only works if both groups = same n

Question 20

whats the paired-samples t-test formula?

Answer 20

same structure of the t-test but now tests whether the average difference score is significantly different from chance

Question 21

what cant the t-test tell us?

Answer 21

• how meaningful the effects really are

- > bc its highly dependant on the sample size

Question 22

effect size measures:

1. cohen’s d gives?
2. small, medium & large effect sizes

Answer 22

1. estimate of effect size independent of the sample size
2. -0.2
   - 0.5
   - 0.8

Question 23

effect size measures:

1. variance explained r2 gives
2. small, medium & large effect sizes

Answer 23

1. estimate effect size NOT independent of the sample size
2. • 0.01
     - 0.09
     - 0.25

Question 24

What’s a confidence interval?

-> why set it?

Answer 24

• range of values centered around a sample statistic
• should be near to the corresponding population parameter
• > to make sure our population mean is contained somewhere within that interval (e.g. 95% confidence interval -> make sure our pop.mean was contained within in 95%

Question 25

Assumptions to consider before deciding whether to run a t-test (3)

Answer 25

1. observations must be independent (no systematic biases)
2. populations must be normal
3. if comparing 2 populations, samples must have equal variances