lecture 22

Question 1

what is internal validity

Answer 1

whether the study design, conduct, and analysis answer the research questions without bias

Question 2

what is external validity

Answer 2

whether the findings can be applicable to broader populations, also known as generalisability

Question 3

what does increasing the size of a sample when random sampling do

Answer 3

• reduces sample variability
• increases likelihood of getting a representative sample
• increases precision of parameter estimate

Question 4

what is estimating population parameters

Answer 4

when you use the findings from your study population to estimate what would occur in the source population

Question 5

what are the 2 methods of measuring the influence of sampling error

Answer 5

confidence intervals
p values

Question 6

how does a 95% confidence interval work

Answer 6

if your repeated a study 100 times with a random sample each time, 100 estimates and 100 confidence intervals

then….

• 95 of them = the parameter would lie within that studies 95% confidence interval
• 5 of them = the parameter would not lie within the studies 95% confidence interval

Question 7

what is an interpretation of what a confidence interval is

Answer 7

we are 95% confident that the true population value lies between the limits of the confidence interval

Question 8

what does a smaller confidence interval mean

Answer 8

more precise

Question 9

what does increasing the sample size of a study do to the confidence interval

Answer 9

increasing the sample size can make the confidence interval narrower

Question 10

what can a confidence interval help us decide about a study

Answer 10

whether it is clinically important

Question 11

what is the estimate or point estimate

Answer 11

the measure found in the study sample

Question 12

what is the parameter

Answer 12

the true value of the measure in the population that the study is trying to discover

Question 13

what do confidence intervals give us a sense of

Answer 13

how precisely we are estimating the population parameter

Question 14

what are p vales

Answer 14

probability of getting study estimate (or one further from the null) when there is really no association, just because of sampling error (chance)

Question 15

what logic do p values use

Answer 15

logic of hypothesis testing

Question 16

in the 2 by 2 table of association vs no association what do p-values tell us about

Answer 16

when we find association in the study results and there is no association in the parameter

Question 17

what is the null hypothesis

Answer 17

when there really is no association in the population

Question 18

when the parameter equals null values the ratio and difference measures equal ….

Answer 18

odds ratio and relative risk = 1

risk difference = 0

Question 19

what is the alternative hypothesis

Answer 19

when there really is an association in the population

Question 20

when the parameter does not equal the null value the ratio and difference measures will equal ….

Answer 20

odds ratio and relative risk = will not equal 1

risk difference = will not equal 0

Question 21

what is the symbol for null hypothesis

Answer 21

Ho

Question 22

what is the symbol for alternative hypothesis

Answer 22

HA

Question 23

example of interpreting a p value when the …

odds ratio = 2.3
p = 0.03

Answer 23

the probability of finding an OR of 2.3 (or further from the null) when the null hypothesis is true is 0.03 (or 3%)

Question 24

what is the threshold for p values

Answer 24

0.05

Question 25

what is said if the p value is less than 0.05

Answer 25

• reject Ho
• accept HA

association is ‘statistically significant’

Question 26

what is said if the p value is greater than 0.05

Answer 26

• fail to reject Ho
• reject HA

association is ‘not statistically significant’

Question 27

interpretation of p value when it is less than 0.05

Answer 27

since the p value is less than 0.05 the association is statistically significant. chance is an unlikely explanation of the study finding

Question 28

interpretation of a p value when it is greater than 0.05

Answer 28

since the p value is greater than 0.05 the association if not statistically significant. the study finding is consistent with chance as an explanation

Question 29

what are type II errors

Answer 29

when incorrectly fail to reject Ho when should have

(p should have been < 0.05 but got > 0.05)

Question 30

what are type II errors usually from

Answer 30

typically due to having too few people in the study

Question 31

what does sample size mean for the p value

Answer 31

bigger sample size = more likely to get small p value

smaller sample size = less likely to get small p value

Question 32

what is said if the 95% confidence interval includes the null value

Answer 32

p > 0.05

not statistically significant

Question 33

what is said if the 95% confidence interval doesn’t include the null value

Answer 33

p < 0.05

statistically significant

Question 34

how should you report p values

Answer 34

useful to report p values not just statistically or not statistically significant

Question 35

why are p values problematic

Answer 35

• arbitrary threshold
• only about Ho = just gives evidence about consistency with the null hypothesis
• nothing about importance

Question 36

what should be considered when evaluating internal validity

Answer 36

• chance = confidence intervals
• bias = strengths and limitations
• confounding = comparing analysis phase (like multivariable analysis etc)