Statistics - definitions

Question 1

Power of a study

Answer 1

Probability of correctly rejecting the null hypothesis (that there is no difference between treatment arm and placebo) when it is false (i.e. not making a type II error)

Question 2

Standard error

Answer 2

Used in the calculation of confidence interval – many sample means

Question 3

Type I error + not affected by + increases with..

Answer 3

This is where the null hypothesis (that there is no difference between remdesivir and placebo) is incorrectly rejected (false positive result).
Termed alpha
Not affected by sample size as significance level determined in advance. The chance increases if the number of end points is increase

Question 4

Type II error
determined by

Answer 4

The failure to reject the null hypothesis (that there is no difference between remdesivir and placebo) when it is false (also known as a false negative result).
Termed beta
Determined by sample size and alpha (type I error)

Question 5

P Value

Answer 5

Probaility of obtaining a result by chance at least as extreme as te one that has actually observed, assuming that the null hypothesis is true

Question 6

Properties of Normal distribution

Answer 6

Symmetry = Mean= median = Mode

68.3% of values lie within 1 SD of the mean
95.4% of values lie within 2 SD of the mean
99.7% of values lie within 3 SD of the mean

Question 7

95% confidence interval

Answer 7

IF a repeat sample of 100 observations are taken from the same group 95 of them would be expected to lie in that range

Question 8

Standard deviation

Answer 8

IS a measure of how much dispersion exists from the mean

Question 9

Parametric Data tests

Answer 9

Students t-test – paired or unpaired
Pearsons product-moment coefficient – correlation

Question 10

Non-parametric tests

Answer 10

Mann-Whitney U test
Wilcoxon signed-rank test
Chi squared tests
Spearman, kendall rank- correlation

Question 11

Endemic

Answer 11

Persistent, usual, or expected level of disease in a given population

Question 12

Pandemic

Answer 12

Involves epidemics affecting a large number of people across many countries, continents or regions

Question 13

Validity

Answer 13

Refers to the extent to which something measures what it claims to measure. First major distinction is internal and external validity

Question 14

Reliability

Answer 14

Is the extent to which an experiment, test, or any measuring procedure yields the same result on repeat trials

Question 15

Internal validity

Answer 15

Is the confidence that we can place in the cause and effect relationship in a study. It is the confidence that we have that the change in the independent variable caused the observed change in the dependent variable (rather than due to poor control of extraneous variables

Question 16

Internal validity
example

Answer 16

A researcher has suggested a causal link between a new designer drug and psychosis. During peer review of the research, this link is challenged by the fact that the temporal relationship between to the two has not been proven. What form of validity has been brought into question?

Question 17

External Validity

Answer 17

IS the degree to which the conclusions in a study would hold for other persons in other places and at other times i.e. its ability to generalize

Question 18

external validity example

Answer 18

A PhD student conducted a small study which demonstrated that a new form of psychological therapy successfully alleviated symptoms of depression. They therefore made a claim in a review article that the new therapy is successful in treating depression. They were criticised for making this claim as the therapy had not been widely tested and so the results may not be generalisable.

Question 19

Content validity

Answer 19

Content validity refers to the extent to which a test or measure assesses the full content of a subject or area.

Question 20

content validity example

Answer 20

For example if a test is designed to help diagnose depression, it would have poor content validity if it only asked about psychological symptoms and neglected biological ones

Question 21

Face validity

Answer 21

Face validity refers to the general impression of a test. A test has face validity if it appears to test what it is meant to

Question 22

Criterion validity

Answer 22

Criterion validity concerns the comparison of tests. You may wish to compare a new test to see if it works as well as an old, accepted method. The correlation coefficient is used to test such comparisons

Question 23

Criterion validity (concurrent)

Answer 23

In concurrent validation, the predictor and criterion data are collected at or about the same time.

Question 24

Criterion validity (concurrent) example

Answer 24

An example could be testing a new, shorter test of intellectual functioning against a standard measure

Question 25

Criterion validity (predictive)

Answer 25

In Predictive validation, the predictor scores are collected first and criterion data are collected at some later/future point. Here you want to know if the test predicts future outcomes.

Question 26

Criterion validity (predictive) example

Answer 26

An example might be evaluating a new assessment method to select medical students. The test could be compared against the students performance at the end of year one to see if there is a correlation

Question 27

Construct validity

Answer 27

The extent to which a test measures the construct it aims to

Question 28

Construct validity (convergent)

Answer 28

A test has convergent validity if it has a high correlation with another test that measures the same construct

Question 29

Construct validity (divergent)

Answer 29

A test’s divergent validity is demonstrated through a low correlation with a test that measures a different construct

Question 30

Nominal data types

Answer 30

Observed values can be put into set categories which have no particular order or hierarchy. You can count but not order or measure nominal data (for example birthplace)

Question 31

Ordinal data types

Answer 31

Observed values can be put into set categories which themselves can be ordered (for example NYHA classification of heart failure symptoms)

Question 32

Discrete data types

Answer 32

Observed values are confined to a certain values, usually a finite number of whole numbers (for example the number of asthma exacerbations in a year)

Question 33

Continuos data types

Answer 33

Data can take any value with certain range (for example weight)

Question 34

Binomial data types

Answer 34

Data may take one of two values (for example gender)

Question 35

Interval data types

Answer 35

A measurement where the difference between two values is meaningful, such that equal differences between values correspond to real differences between the quantities that the scale measures (for example temperature)

Question 36

A Rate

Answer 36

is a quantity measured with respect to another measured quantity (e.g. 60 miles an hour).

Question 37

Attributable risk

Answer 37

is the rate in the exposed group minus the rate in the unexposed group. For example the attributable risk for lung cancer in smokers is the rate of lung cancer in smokers minus the rate of cancer in non smokers. Essentially it tells you what proportion of deaths in the exposed group were due to the exposure.

Question 38

Relative risk

Answer 38

is the risk of an event relative to exposure. It is also known as the risk ratio.

Question 39

Population attributable risk

Answer 39

can be described as the reduction in incidence that would be observed if the population were entirely unexposed. For instance how would the incidence of lung cancer change if everyone stopped smoking? It can be calculated by multiplying the attributable risk by the prevalence of exposure in the population.

Question 40

Attributable proportion

Answer 40

is the proportion of the disease that would be eliminated in a population if its disease rate were reduced to that of the unexposed group.

Question 41

False-negative rate

Answer 41

Would be the proportion of patients with a disease not picking up by testing

Question 42

False-positive rate

Answer 42

proportion of patients with a positive test who do not have the condition being tested for

Question 43

Hazard ratio

Answer 43

The hazard ratio (HR) is similar to relative risk but is used when risk is not constant to time. It is typically used when analysing survival over time

Question 44

Precision

Answer 44

The precision quantifies a tests ability to produce the same measurements with repeated tests.

Question 45

Confounding

Answer 45

Refers to a variable which correlates with other variables within a study leading to spurious results

Question 46

Confounding

Answer 46

Refers to a variable which correlates with other variables within a study leading to spurious results
Confounding occurs when there is a non random distribution of risk factors in the populations. Age, sex and social class are common causes of confounding.

Question 47

Confounding can be controlled by (design stage versus analysis stage)

Answer 47

In the design stage = randomisation

In the analysis stage of an experiment, confounding can be controlled for by stratification.