Categorical: 
Qualitative 
 Nominal 
· Labelled ·
No quantity or order 
 Ordinal 
· Numbered ·
Variable increments 
Numerical: 
Quantitative 
 Discrete 
·
Whole numbers only 
 Continuous 
· Any value · Constant increment · Interval data: false zero point (i.e. 20°C isn’t twice as hot as 10°C) · Ratio data: true zero point (e.g. 2/52 is twice as old as 1/52) 

2 groups 
>2 groups 

Paired 
Unpaired 
Paired 
Unpaired 

Parametric 
Student T 
Student T 
ANOVA 
ANOVA 
Nonparametric Nominal 
McNemar 
Chi 
Cochrane Q 
Cochrane Q 
Nonparametric Ordinal / ratio 
Wilcoxon Rank Sum 
Mann Whitney U 
Friedman 
Kruskell Wallis 
N.B. Parametric tests are used when data a) are numerical b) follow a distribution
Mean 
· Sum / number of observations · More affected by outliers 
Median 
· Half the observations are higher, other half are lower · Less affected by outliers 
Mode 
· Most frequently occurring value · Rarely relevant 
Interquartile range 
· Middle 50% of observations · Often represented on box and whisker plot o Box: 25, 50 and 75 o Whiskers: 10 and 90 
Standard deviation 
· SD = √variance ·
Variance = ε(xẋ)^{2}/(n1), where 
Standard error 
·
SE = SD/√n, where · Indicates how far the sample mean is likely to be from the true mean · Used to derive confidence interval 
Confidence interval 
·
CI = mean ± z(SD/√n) · Indicates a range within the true value is likely to fall · Indicates both magnitude and certainty of difference (cf. P value) · If the confidence interval crosses 1.0, result is insignificant · Can be calculated for anything: mean, odds ratio, relative risk etc 
Example:

Pain 
No pain 
Fentanyl 
A 
B 
Nothing 
C 
D 
Relative risk 
· Risk of pain in fentanyl group = A/(A+B) = R_{F} · Risk of pain in nothing group = C/(C+D) = R_{N} · Relative risk = R_{F}/R_{N} · The risk of the event in the intervention group compared with the risk of the even in the control group” · Amplifies the apparent effect of a drug on rare outcomes o e.g. if 1% to 0.3%: RRR is 70%, ARR is 0.3% 
Odds ratio 
· Odds of pain in fentanyl group = A/B = O_{F} · Odds of pain in nothing group = C/D = O_{N} · Odds ratio = O_{F}/O_{N} · “A ratio of event to nonevent in the intervention group compared with the control group” · Almost the same as RR if large data set and rare event 
Hazard ratio 
· Hazard ratio is the relative risk of an event happening at time t · i.e. risk of pain now cf. risk of pain at some point 
Number needed to treat 
· Absolute risk reduction = R_{N}  R_{F} · NNT = 1 / (absolute risk reduction) 
Type 1 error 
· False positive ·
Threshold (α value) usually set 5% 
Type 2 error 
· False negative ·
Threshold (β value) usually set at 20% 
Power 
· The ability to detect a difference if there is one · Power = 1 – β value = usually 80% Determinants: · Sample size (for 2x precision, need 4x numbers) · Magnitude of difference · Threshold for effect · (many others) 
P value 
· Probability of finding this result (or greater) by chance if the null hypothesis were true · i.e. probability of this being a false positive result Problems with p = 0.05 · No account of prior probability · No indication of effect size · Not low enough if the stakes are high · Inappropriate for multiple comparisons (see Bonferroni correction) 
Fragility index 
· Number of patients whose change in status would turn a significant result into a nonsignificant result 
Bonferroni correction 
· Used if assessing for several outcomes simultaneously · Divide α (p = 0.05) by the number of tests being performed 
Rule of 3’s MCQ 
· If no events in the entire population, 95% confidence interval is <3 · Applies no matter how many in the population · Derived from binomial theory 

Screening 
Diagnostic 
Target 
Everyone 
Symptomatic; or Positive screening test 
Nature 
Noninvasive 
Invasive 
Thresholds 
High sensitivity (few false neg) 
High specificity (few false pos) 
Cost 
Cheap 
Expensive 

Difficult ETT 
Not difficult ETT 
Predictive values: 
High MP score 
a)True +ve 
b)False +ve 
PPV: a/(a+b) 
Low MP score 
c)False ve 
d)True ve 
NPV: d/(c+d) 

Sensitivity: a/(a+c) 
Specificity: d/(b+d) 






· Positive LR = sensitivity / (1specificity) · Negative LR = (1sensitivity) / specificity 




Sensitivity 
· If have disease, how likely is the test to agree · SNOUT: SeNsitive test when negative rules OUT the disease 
Specificity 
· if don’t have disease, how likely is the test to agree · SPIN: a SPecific test when positive rule IN the disease 
PPV 
· If test says yes disease, how likely to have disease · ∝ prevalence as well as test quality 
NPV 
· If test says no disease, how likely to not have disease · ∝ rareness as well as test quality 
Receiver operator characteristic 
· Relationship between sensitivity, specificity, test quality · ↑AUC associated with high utility

Youden’s J statistic 
· J = sensitivity + specificity  1 · J = point of maximum divergence of the curve · Single representation of a test’s utility, between 0 to 1 
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