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Sifting the evidence—what's wrong with significance tests?

Department of Social Medicine, University of Bristol, Bristol BS8 2PR
Jonathan A C Sterne senior lecturer in medical statistics
George Davey Smith professor of clinical epidemiology

Correspondence to: J Sterne jonathan.sterne@bristol.ac.uk

Accepted November 9, 2000

Because this article has no abstract, we have provided an extract of the full text and any section headings.

	Introduction

 
The findings of medical research are often met with considerable scepticism, even when they have apparently come from studies with sound methodologies that have been subjected to appropriate statistical analysis. This is perhaps particularly the case with respect to epidemiological findings that suggest that some aspect of everyday life is bad for people. Indeed, one recent popular history, the medical journalist James Le Fanu's The Rise and Fall of Modern Medicine, went so far as to suggest that the solution to medicine's ills would be the closure of all departments of epidemiology.¹

One contributory factor is that the medical literature shows a strong tendency to accentuate the positive; positive outcomes are more likely to be reported than null results.^2–4 By this means alone a host of purely chance findings will be published, as by conventional reasoning examining 20 associations will produce one result that is "significant at P = . . . [Full Text of this Article]

	P values and significance testing—a brief history

 

	Misinterpretation of P values and significance tests

 

	How significant is significance?

 

	Interpreting P values: opinions, decisions, and the role of external evidence

 

	What is to be done?

 
D R Cox

Nuffield College, Oxford OX1 1NF

D R Cox professor
david.cox@nuf.ox.ac.uk

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