Practice

Statistics Notes

Parametric v non-parametric methods for data analysis

BMJ 2009; 338 doi: 10.1136/bmj.a3167 (Published 2 April 2009)
Cite this as: BMJ 2009;338:a3167

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  1. Douglas G Altman, professor of statistics in medicine1,
  2. J Martin Bland, professor of health statistics2
  1. 1Centre for Statistics in Medicine, University of Oxford, Wolfson College Annexe, Oxford OX2 6UD
  2. 2Department of Health Sciences, University of York, York YO10 5DD
  1. Correspondence to: Professor Altman doug.altman{at}csm.ox.ac.uk

    Continuous data arise in most areas of medicine. Familiar clinical examples include blood pressure, ejection fraction, forced expiratory volume in 1 second (FEV1), serum cholesterol, and anthropometric measurements. Methods for analysing continuous data fall into two classes, distinguished by whether or not they make assumptions about the distribution of the data.

    Theoretical distributions are described by quantities called parameters, notably the mean and standard deviation.1 Methods that use distributional assumptions are called parametric methods, because we estimate the parameters of the distribution assumed for the data. Frequently used parametric methods include t tests and analysis of variance for comparing groups, and least squares regression and correlation for studying the relation between variables. All of the common parametric methods (“t methods”) assume that …

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