Medical statistics
See also Wiki page on "biostatistics" and links below.
Hypothesis testing
Hypotheses are almost impossible to prove, much easier to disprove. Hypothesis testing usually attempts to disprove a null hypothesis. If p >= 0.05, then a study is generally considered to have failed to disprove the null hypothesis.
Types of data
 Categorical, including nominal and ordinal
 Interval (32, 43…) or ratio (6 = 2*3, 9 = 3*3, 12 = 4*3 = 2*6).
 Continuous
Estimates
Estimates.
 Precision (scatter of an estimate)
 accuracy (amount of bias)
Describing a population
 μ (mu)
 = mean of population
 σ (sigma)
 = standard deviation of population
 μ ± 2σ
 = confidence interval for μ
Causation
Features that suggest that a causal relationship include:
 the strength of the relationship;
 the consistency (in different trials etc.  inconsistency may indicate bias) of the relationship;
 its specificity (cause > only one effect, effect due to single cause);
 the temporal relationship;
 the biologic gradient (doseresponse);
 biological plausibility;
 coherence;
 evidence from experiments;
 analogy
Interaction
American term "effect modification" may be preferable. Occurs when the effect is different in different groups, e.g if a drug is harmful in children, progressively less harmful in older age groups, and useful in the elderly. Age "interacts" with the effects of the drug.
Common statistical tests
Trisha Greenhalgh has published a useful list of tests in her "how to read a paper" series in the BMJ  see below. The following table is based on one from this paper.
Some commonly used statistical tests  table
Parametric test  Example of nonparametric  Purpose of test  Example 

Twosample (unpaired) t test  MannWhitney U test  Compares two independent samples drawn from the same population  To compare girls’ heights with boys’ heights 
One sample (paired) t test  Wilcoxon matched pairs test  Compares two sets of observations on a single sample  To compare weight of infants before and after a feed 
One way analysis of variance (F test) using total sum of squares  KruskalWallis analysis of variance by ranks  Effectively, a generalisation of the paired t or Wilcoxon matched pairs test where three or more sets of observations are made on a single sample  To determine whether plasma glucose is higher one, two, or three hours after a meal 
Two way analysis of variance  Two way analysis of variance by ranks  As above, but tests the influence (and interaction) of two different covariates  In the above example, to determine whether the results differ in male and female subjects 
χ^{2} test  Fisher’s exact test  Tests the null hypotheses that the distribution of a discontinuous variable is the same in two (or more) independent samples  To determine whether acceptance into medical school is more likely if the applicant was born in Britain 
Product moment correlation coefficient (Pearson’s r)  Spearman’s rank coefficient (r^{2})  Assesses the strength of the straight line association between two continuous variables  To assess whether and to what extent plasma HbA1 concentration is related to plasma triglyceride concentration in diabetic patients 
Regression by least squares method  Nonparametric regression (various tests)  Describes the numerical relation between two quantitative variables, allowing one value to be predicted from the other  To see how peak expiratory flow rate varies with height 
Multiple regression by least squares method  Nonparametric regression (various tests)  Describes the numerical relation between a dependent variable and several predictor variables (covariates)  To determine whether and to what extent a person’s age, body fat, and sodium intake determine their blood pressure 
Variance, standard error of the mean
See Variance and standard error of the mean
χ^{2} test
See Chi square test. See also Wiki page on Chi Square
Student’s ttest
See Statistical tests for comparing means
MannWhitney U Test
See Statistical tests for comparing means
Paired ttest
See Statistical tests for paired or matched data  Paired ttest
Wilcoxon test
See Statistical tests for paired or matched data  Wilcoxon test
Pearson product moment coefficient
See Statistical tests for product moment coefficients  Pearson product moment coefficient.
Spearman correlation
Spearman correlation coefficient is a nonparametric equivalent of Pearson product moment coefficient.
See Statistical tests for product moment coefficients  Spearman correlation.
McNemar test
Used for proportions in matched groups. See McNemar test.
Regression
Including analysis of variance. See Statistical tests for regression.
Survival analysis
See Statistical tests for survival analysis. May include analysis of regression to identify risk factors.
Kappa test
The kappa (κ) test is a test of agreement  e.g. between experts, sphygmomanometers.
See Statistical tests for agreement  Kappa test.
Metaanalysis
This is a statistical technique which assumes the study populations in a number of clinical trials are similar and examines the pooled outcomes. It can be extremely useful when a number of randomised controlled trials have collected data on an issue, in which any one trial is underpowered to detect a clinically significant effect in the variable of interest. For a fuller account try What is metaanalysis?
Internet resources on medical statistics
 Statistics at Square One by T D V Swinscow, revised by M J Campbell, University of Southampton, published by BMJ is available online (currently  October  9th edition)
 Steve's attempt to teach statistics (or here)
 HyperStat Online Statistics Textbook
 Statistics jokes
 StatPages "Web pages that perform statistical calculations!"  claims (October) to have "Over 600 Links (including 380 Calculating Pages)  And Growing!" (previously here, with the same claim)
 Dr Robert Newcombe has lots of spreadsheets for downloading, for various statistical calculations at his website.
 Supercourse has lectures on "biostatistics".
 Medpage Guide to Biostatistics  covering study design, research methods, and many aspects of medical statistics
 Trisha Greenhalgh has written an excellent series of papers "How to read a paper" in the BMJ, including:

 The Medline database (BMJ 1997;315:180183 (19 July))
 Getting your bearings (deciding what the paper is about) (BMJ 1997;315:243246 (26 July))
 Assessing the methodological quality of published papers (BMJ 1997;315:305308 (2 August))
 Statistics for the nonstatistician. I: Different types of data need different statistical tests (BMJ 1997;315:364366 (9 August))
 Statistics for the nonstatistician. II: "Significant" relations and their pitfalls (BMJ 1997;315:422425 (16 August))
 Papers that report drug trials (BMJ 1997;315:480483 (23 August))
 Papers that report diagnostic or screening tests (BMJ 1997;315:540543 (30 August))
 Papers that tell you what things cost (economic analyses) (BMJ 1997;315:596599 (6 September))
 Papers that summarise other papers (systematic reviews and metaanalyses) (BMJ 1997;315:672675 (13 September))
 Papers that go beyond numbers (qualitative research) (BMJ 1997;315:740743 (20 September))