Proof, p-values, and hypothesis testing
- Author
- David M Rind, MD
David M Rind, MD
- Section Editor — General Medicine
- Chief Medical Officer
- Institute for Clinical and Economic Review
- Assistant Professor of Medicine, Part-time
- Harvard Medical School
- Section Editor
- Joann G Elmore, MD, MPH
Joann G Elmore, MD, MPH
- Editor-in-Chief — Primary Care (Adult)
- Section Editor — General Medicine
- Professor of Medicine, Adjunct Professor of Epidemiology
- University of Washington School of Medicine
- Deputy Editor
- Carrie Armsby, MD, MPH
Carrie Armsby, MD, MPH
- Senior Deputy Editor — UpToDate
- Deputy Editor — Pediatrics
- University of Massachusetts School of Medicine
INTRODUCTION
The concepts around biostatistics are frequently confusing to clinicians. The meaning of a p-value in particular is commonly misunderstood and yet is central to the way most clinicians interpret the results of scientific studies [1,2].
This review will discuss the correct interpretation of p-values and confidence intervals, the idea of proof, and the understanding of power calculations in negative studies. A general discussion of the meaning of biostatistical terms is found elsewhere. (See "Glossary of common biostatistical and epidemiological terms".)
PROOF
In scientific and medical endeavors, a common question to be addressed is "what constitutes proof?" How do we decide when the evidence for or against a hypothesis is adequate to consider the matter proven?
Certain methodologies of clinical trials are considered "stronger" than other methodologies. For instance, randomized clinical trials are generally considered better evidence than case control studies. Proof, however, never exists in a single trial result or a single piece of evidence. Proof is a human concept having to do with the rational thought process. Information may be sufficient to allow one person to consider something proven where another will not.
As an example, there is no evidence from clinical trials in humans that cigarette smoking causes lung cancer. However, evidence from epidemiologic studies overwhelmingly shows a relationship between smoking and lung cancer. A dose-response relationship in these studies and evidence from animal studies provide strong support for the relationship having biologic plausibility and being causal (ie, smoking is not just associated with lung cancer but is a cause of lung cancer). Most people consider it proven that smoking causes lung cancer despite the absence of clinical trials in humans.
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To continue reading this article, you must log in with your personal, hospital, or group practice subscription. For more information or to purchase a personal subscription, click below on the option that best describes you:Literature review current through: Jun 2017. | This topic last updated: Apr 07, 2016.The content on the UpToDate website is not intended nor recommended as a substitute for medical advice, diagnosis, or treatment. Always seek the advice of your own physician or other qualified health care professional regarding any medical questions or conditions. The use of this website is governed by the UpToDate Terms of Use ©2017 UpToDate, Inc.References- Davidoff F. Standing statistics right side up. Ann Intern Med 1999; 130:1019.
- Goodman SN. Toward evidence-based medical statistics. 1: The P value fallacy. Ann Intern Med 1999; 130:995.
- Goodman SN, Berlin JA. The use of predicted confidence intervals when planning experiments and the misuse of power when interpreting results. Ann Intern Med 1994; 121:200.
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