A) Parametric statistics

Question 1

independent groups

Answer 1

sample split into two groups. each group does one of the conditions. aka between-subject design

Question 2

matched pairs

Answer 2

same as independent groups however, each ppt is matched on important characteristics with someone in the other group

Question 3

repeated measures

Answer 3

the same group of ppt takes part in both conditions

Question 4

what statistical test for independent groups (parametric and non parametric)

Answer 4

parametric: independent samples t-test
non parametric Mann-Whitney

Question 5

what statistical test for matched pairs (parametric and non parametric)

Answer 5

parametric: paired samples t test
non parametric: Wilcoxon

Question 6

what statistical test for repeated measures (parametric and non parametric)

Answer 6

parametric: paired samples t test
non parametric: wilcoxon

Question 7

Difference between parametric tests and non parametric tests

Answer 7

• parametric tests eg t tests are calculated from the data (using mean and standard error)
• non parametric tests eg Mann Whitney are computed from ranked scores, not using the actual data helps to prevent outlier scores impacting the analysis

Question 8

what do parametric statistics assume

Answer 8

assume the data you have collected come from a population that can be modelled on a normal distribution

Question 9

what do non-parametric statistics assume

Answer 9

are sometimes referred to as ‘distribution-free’ because they do not make that assumption

Question 10

what are parametric tests preferred

Answer 10

they are more sensitive/powerful
if there is a true difference between conditions, a parametric test is more likely to find that difference

Question 11

what assumptions need to be met to use a parametric statistics (3)

Answer 11

1. data needs to be at least interval level (continuous)
2. assume the data collected comes from a population that can be modelled on ‘normal distribution’
3. if comparing two groups, it assumed that they have similar variance

Question 12

what data is appropriate for t-test (4)

Answer 12

• nominal- info is put into categories or named
  -ordinal - info or scores are put in order or ranked
  -interval- there are equal intervals on a measurement scale
  -ratio- same as interval but there is a true zero point

Question 13

what is a normal distribution

Answer 13

bell shaped curve
symmetrical distribution around the centre of all scores

Question 14

skew distribution explanation

Answer 14

more developed on one side or in one direction than another, not symmetrical

Question 15

kurtosis distribution explanation

Answer 15

the sharpness of the peak of a frequency-distribution curve - pointless/heaviness of tails

Question 16

negative skewness values

Answer 16

pile up of scores of the right, tail to the left

Question 17

positive skewness values

Answer 17

pile up of scores on the left, tail to the right

Question 18

two tests of assessing normality

Answer 18

• numerical methods (eg skew/kurtosis values and statistical tests)
• graphical methods (eg visual inspection of graphs)

Question 19

outliers meaning

Answer 19

they impact mean and standard deviation. mean and strd deviation are used to calculate t-test. therefore the presents of outliers biases both descriptives and inferential stats.

Question 20

homogeneous variance

Answer 20

both groups have similar variance

Question 21

heterogeneous variance

Answer 21

the groups have different variance

Question 22

para vs non-parametric (3)

Answer 22

• parametric analyses can analyse non normal distributions for many datasets
• nonparametric analyses have other firm assumptions that can be harder to meet
• the answer is often contingent upon whether the mean or median is a better measure of central tendency for the distribution of your data