statistical tests

Question 1

T-tests

Answer 1

quantitative method- confidence intervals and hypothesis test for mean difference between 2 groups

Question 2

paired T-test

Answer 2

confidence intervals an hypothesis test for mean difference between two paired groups

e. g. participants paired on same criteria
e. g. measurements taken before and after intervention

Question 3

unpaired t-test

Answer 3

confidence intervals and hypothesis test for mean diff between two independent groups

Question 4

assumptions of unpaired t-tests

Answer 4

• normal distribution
• SD is similar in two groups
• participants are independent between groups

Question 5

Analysis of variance

Answer 5

• A method for hypothesis testing
• Anova
• Unpaired groups
• Provides a global p-value comparing the mean across all groups

Question 6

repeated measures analysis of variance

Answer 6

hypothesis test for comparing the mean across three or more paired groups

Question 7

assumptions for repeated measures analysis of variance

Answer 7

Ð the difference scores between any two groups are Normally distributed in the population (or sample size is large and difference scores not too skewed)
Ð the standard deviation of the difference scores when comparing any two groups should be similar (“sphericity” assumption)

Question 8

non-parametric methods for comparing groups (4)

Answer 8

Mann Whitney test, Wilcoxon signed ranks, Kruskal-wallis, Friedman test

Question 9

what do non-parametric methods do

Answer 9

compare entire distributions and not means between groups- used when data is not normally distributed

Question 10

non-parametric methods summarise their groups using

Answer 10

medians and interquartile ranges

Question 11

which non-parametric test could be used when comparing two groups

Answer 11

Mann Whitney test

Question 12

which non-parametric test cold be use to compare paired groups

Answer 12

Wilcoxon signed ranks test

Question 13

which non-parametric test could be used to compare three or more independent groups

Answer 13

Kruskal wallis test

Question 14

which non-parametric test could be used to compare three or more paired groups

Answer 14

Friedman test

Question 15

parametric tests

Answer 15

T-test and analysis of variance

Question 16

parametric tests make assumptions based on

Answer 16

they make distribution assumptions e.g. why data has to be normally distributed

Question 17

what methods are used to summarise the groups in parametric tests

Answer 17

SD and means

Question 18

when should non-parametric tests be used

Answer 18

when the assumptions that underlie parametric methods for independent groups don’t hold: skewed, small sample, SD differ markedly

Question 19

advantage of non-parametric methods

Answer 19

o Always valid for quantitative data (even skewed data in small samples and ordinal data)
o Where the assumptions of parametric methods are met non-parametric methods often provide similar p-values

Question 20

disadvantage son non-parametric

Answer 20

o They do not make direct inferences about a parameter, such as the mean difference
o Provide no confidence intervals, only p-values
o Based only on the analysis of ranks, not actual scores
o When assumptions for parametric methods hold, non- parametric methods can be less sensitive

Question 21

box and whisker plot are used to show

Answer 21

• median
• lower quartile
• upper quartile

Question 22

outliers

Answer 22

extrem observations with every low or very high values

Question 23

positively skewed distribution on a box and whisker plot are shown by

Answer 23

top part being thicker than bottom part- whisker is slightly longer than the bottom whisker

Question 24

correlation

Answer 24

is the association between 2 variables- the extent to which higher values of one variables occurs in combination with higher values on other variables

Question 25

how is correlation presented graphically

Answer 25

scatterplots

Question 26

how is correlation presented numerically

Answer 26

correlation coefficients

Question 27

persons correlation coefficients

Answer 27

quantifies the strength of association between two quantitative variables which have a linear relationship. Between 0 and 1.

Question 28

Spearman

Answer 28

quantifies non-linear and monotonic reltionships

Question 29

R2

Answer 29

is the proportion of the variation in one variable that is explain day another variable

Question 30

R2 is known as

Answer 30

the coefficient of determination (RxR)

Question 31

linear regression

Answer 31

a mathematical equation which describes the linear relationship between a quantitative outcome and quantitative predictor.

Question 32

in linear regression he predictors is often

Answer 32

assumed to be a potential cause of the otucome

Question 33

linear regression equation

Answer 33

outcome= a + b x predictor

a and b are recession coefficients_

Question 34

a is called the

Answer 34

constant or intercept (mean value of outcome when predictor is zero)

Question 35

b is called the

Answer 35

slope

the predicted increase i the outcome for each one unit increase in the predictor

Question 36

diff between pearl and repression slope

Answer 36

¥ Pearson correlation coefficient (r) quantifies strength of association
¥ regression slope (b) describes the relationship and can be used to predict the outcome variable score based on the predictor variable score

Question 37

assumptions for regression (3)

Answer 37

relationship between the outcome and quantitative predictor is linear

2) residuals are normally distributed
3) constant variance (homoscedasticity)

Question 38

homoscedasticity

Answer 38

the variability in the residuals is the same across the predicted (fitted) values

Question 39

spear correlation

Answer 39

can take value between -1 and 1. Used for non-linear associations provided they are monotonic

Question 40

monotonic

Answer 40

means that either the relationship in the scatterplot is never positive or never negative

Question 41

Chi-squared

Answer 41

is a parametric method, tests quantify evidence against the null hypothesis
based on the discrepancy between numbers observed in each cell of 2 by 2 table and the numbers expected if null hypothesis is true
greater the discrepancy the smaller the p-value and the greater the evidence against the null hypothesis

Question 42

assumptions of Chi-squared

Answer 42

total sample size of at least 40 or if the sample size is between 20 and 39 the expected value in each cell is 5

Question 43

if the assumptions of chi-squared cannot be met…

Answer 43

Fishers exact test is used

Question 44

Fishers exact test

Answer 44

is the non-parametric alternative to the chi squared test be used for 2 by 2 contingency tables

Question 45

when is fishers used

Answer 45

Ð fewer than 20 participants or
Ð between 20 and 39 participants and the expected value in at least one cell is less than 5
Ð fewer than 20 participants or
Ð between 20 and 39 participants and the expected value in at least one cell is less than 5