How to read a paper: Statistics for the non-statistician. I: Different types of data need different statistical tests

doi:10.1136/bmj.315.7104.364

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Please see: Statistics for the non-statistician. I: Different types of data need different tests

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Trisha Greenhalgh, senior lecturer (p.greenhalgh@ucl.ac.uk)a

^a Unit for Evidence-Based Practice and Policy, Department of Primary Care and Population Sciences, University College London Medical School/Royal Free Hospital School of Medicine, Whittington Hospital, London N19 5NF

Introduction

As medicine leans increasingly on mathematics no clinician can afford to leave the statistical aspects of a paper to the “experts.” If you are numerate, try the “Basic Statistics for Clinicians” series in the Canadian Medical Association Journal 1 2 3 4 or a more mainstream statistical textbook.5 If, on the other hand, you find statistics impossibly difficult, this article and the next in this series give a checklist of preliminary questions to help you appraise the statistical validity of a paper.

Have the authors set the scene correctly?

Have they determined whether their groups are comparable, and, if necessary, adjusted for baseline differences?

Most comparative clinical trials include either a table or a paragraph in the text showing the baseline characteristics of the groups being studied. Such a table should show that the intervention and control groups are similar in terms of age and sex distribution and key prognostic variables (such as the average size of a cancerous lump). Important differences in these characteristics, even if due to chance, can pose a challenge to your interpretation of results. In this situation, adjustments can be made to allow for these differences and hence strengthen the argument.6

Summary points

In assessing the choice of statistical tests in a paper, first consider whether groups were analysed for their comparability at baseline

Does the test chosen reflect the type of data analysed (parametric or non-parametric, paired or unpaired)?

Has a two tailed test been performed whenever the effect of an intervention could conceivably be a negative one?

Have the data been analysed according to the original study protocol?

If obscure tests have been used, do the authors justify their choice and provide a reference?

What sort of data have they got, and have they used appropriate statistical tests?

Numbers are often used to label the properties of things. We can assign a number to represent our height, weight, and so on. For properties like these, the measurements can be treated as actual numbers. We can, for example, calculate the …