Testing Groups (2 groups)

Question 1

Test statistic

Answer 1

• (variance explained by model)/(variance not explained by model)
• effect/error

Question 2

Type I error

Answer 2

• occurs when we believe that there is a genuine effect in our population when, in fact, there isn’t
• probability is the alpha-level (usually 0.5)

Question 3

Type II error

Answer 3

• occurs when we believe that there is no effect in the population when, in reality, there is
• the probability is the beta-level (often 0.2)

Question 4

Positive Study

Answer 4

- Significant difference
Truth = difference
- true positive
Truth = no difference
- type I error

Question 5

Negative study

Answer 5

- No significant difference
Truth = difference
- type II error
Truth = no difference
- true negative

Question 6

P - value

Answer 6

the level of marginal significance within a statistical hypothesis test representing the probability of the occurrence of a given event

Question 7

Assumptions of a T-test

Answer 7

• data is measured as quantitative and continuous
• variances in these populations are roughly equal
• measurements in different treatments are independent (most important)
• data must be sampled from a normally distributed population

Question 8

Homogeneity of variance

Answer 8

variances in populations are roughly equal

Question 9

Homoscedasticity

Answer 9

variances in populations are equal

Question 10

Heteroscedasticity

Answer 10

variances in populations are not equal

Question 11

Calculating effect size

Answer 11

• signal/noise

- (difference between groups)/(variability of groups)

Question 12

Rejecting or accepting null hypothesis using P-value

Answer 12

• the likelihood of observing the same or more extreme test statistic by chance alone, when hypothetically there can be no observable difference
• if p < 0.05 we reject our null hypothesis

Question 13

Effect size

Answer 13

a standardised measure of the size of an effect

Question 14

Standardised

Answer 14

comparable across studies

Question 15

Cohen’s d

Answer 15

• an effect size used to indicate the standardised difference between two means

Question 16

Calculating cohen’s d

Answer 16

d = (M1-M2)/SDpooled

Question 17

Calculating SDpooled

Answer 17

sqrt((SD1^2+SD2^2)/2)

Question 18

Pearson’s R

Answer 18

a measure of the linear correlation between two variables X and Y

Question 19

Calculating pearson’s R

Answer 19

1. make a table with x, y, xy, x^2 and y^2 along the top; sample number down side
2. total all the columns
3. (n∑xy-(∑x)(∑y)) / sqrt [n∑x^2-(∑x)^2][n∑y^2-(∑y)^2]

Question 20

Small effect

Answer 20

r = 0.1
d = 0.2
the effect explains 1% of the total variance

Question 21

Medium effect

Answer 21

r = 0.3
d = 0.5
the effect accounts for 9% of the total variance

Question 22

Large effect

Answer 22

r = 0.5
d = 0.8
the effect accounts for 25% of variance

Question 23

Reporting results of a t-test in APA style

Answer 23

the following should be reported:

• t
• df
• difference in means
• SD
• means
• p-value
• effect size (r or d)