Statistics

Question 0

Categorical data

Answer 0

Data that is finite and countable
Children squared
Odds ratios

Question 1

Continuous data

Answer 1

Data that has infinite values
Eg measurements
T squared test
Provide mean difference with confidence intervals

Question 2

Reference ranges

Answer 2

Used to describe the variations of a measurement for a defined population
Taken from standard deviation
Shows consistency of results
And whether it failed in some people

Question 3

Confidence intervals

Answer 3

Use 95%
Means 95% confident that the value lies between the reference ranges
Measure reliability

Question 4

Hypothesis testing

Answer 4

Whether the results happened by chance or whether the association is real
Start with a null hypothesis
Compare actually results with expected results
Work out the value

Question 5

P values

Answer 5

The probability that the observed association is due to chance
P reject null hypothesis

Question 6

Chi squared test

Answer 6

For categorical data that isn’t paired
1) compute expected numbers under null hypothesis
Expected count=row total x column total/ overall total
2) calculate difference between observed and expected valued
X2 = sum of (observed-expected)/ expected
3) look up on value on c squared table for 1 degree of freedom

Question 7

Independent t test

Answer 7

Independent continuous data that is normally distributed

1) difference between means
2) calculate standard error
3) T= difference/SE
4) find p value on t distribution on N-2 degrees of freedom

Question 8

Paired samples t test

Answer 8

Paired continuous data that is normally distributed
Difference between means over standard error
Use n-1 degrees of freedom for finding p value

Question 9

Data that isn’t normally distributed

Answer 9

Try log transforming

Use a parametric test-> Mann-Whitney

Question 10

Correlation

Answer 10

Mutual relation of two of more variables

Doesn’t show causation only association

Question 11

Regression

Answer 11

Measure of relation between the mean values
Used to determine the strength of association
Assumes a one way causal effect
Finds the line of best fit
R= degree of linear relationship
R2= % of variation explained by exposure

Question 12

Prevelance

Answer 12

Number of people with disease/total pop

Question 13

Incidence risk

Answer 13

Number of new cases/total population

Question 14

Incidence rate

Answer 14

Number if new cases/person time at risk

Question 15

Ecological fallacy

Answer 15

Can’t assume the relationship we see can be directly transfer to individual level

Question 16

Relative risk ratio

Answer 16

Association between an exposure and outcome are compared to a baseline group
Risk ratio= risk of event in exposed/risk of event in unexposed
=1-> no difference
1 more likely to have outcome
Categorical data

Question 17

Odds ratio

Answer 17

How likely an event is to happen compared to how likely the event won’t happen
Odds ratio= odds in exposed/odds in unexposed
Mutations be used in case control studies
Categorical data

Question 18

Effect size in continuous data

Answer 18

Mean difference
= 0 no difference
0 outcome is higher in exposed

Question 19

Confidence intervals

Answer 19

For measures of effect
Require a % confidence
Estimate of size is in the middle
If the interval crosses 0 there is no difference as for some people it has had the opposite effect

Question 20

Paired data

Answer 20

Difference of the responses between a pair
Eg before and after, wives and husbands
When we know in advance that observations in one data set are directly related to those in another data set

Question 21

Independent data

Answer 21

Responses of one treatment group compared to another

Two unrelated sets of units are measured

Question 22

Univariate analysis

Answer 22

Descriptive

Summaries data one variable at a time

Question 23

Bivariate analysis

Answer 23

Comparison of 2 groups -> relationship between them

Correlation and measure of effect tests

Question 24

Regression co efficient

Answer 24

Y= a + bx
Regression co efficiently
-> a= y intercept when x=P
-> b= gradient

Question 25

B coefficient

Answer 25

Gradient
An estimate of how much, on average Y increases/ decreases for each unit increase in x
Positive b-> outcome increases as exposure increases-> positive correlation
Negative B-> opposite
B= 0 -> outcome and exposure not related

Question 26

T test for b coefficient

Answer 26

Test of the relationship between the dependent variable and a specific independent variable
T value= b coefficient/ std error of b
Use to find p value

Question 27

Correlation r values

Answer 27

Measures the degree of linear association between two variables
0-0.3 -> weak positive
0.3-0.5 moderate
>0.5 strong 
Still need p values

Question 28

Analysis of variance

Answer 28

Extension to t test
Continuous data
 Compares means in > 2 groups so used for multiple outcome measures
Eg demographic analysis
-> how heterogenous is your group

Question 29

Multiple logistic regression

Answer 29

Linear regression for binary outcomes eg yes/no
Single outcome and more than one independent variable
Still gives you regression co efficients
Used to predict probabilities of different possible outcomes of categorically distributed dependent variable given a set of independent variables

Question 30

Multiple linear regression

Answer 30

Continuous data

Two or more explanatory variables and one response variable

Question 31

Type 1 error

Answer 31

False rejection of a null hypothesis -> find an effect that isn’t there
Often the result of excessive statistical testing
Decease effect by reducing the level of significance
Avoid multiple significance testing or multiple sub group analysis
Defining the hypothesis with primary and secondary outcomes reduces type 1 error

Question 32

Type 2 error

Answer 32

Failure to reject a false null hypothesis -> miss an effect that is there
Not enough power to find significant difference-> sample size to small
Need at least 80% power