This Article
Right arrow PDF
Right arrow Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when eLetters are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Guyatt, G.
Right arrow Articles by Walter, S.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Guyatt, G.
Right arrow Articles by Walter, S.
Related Collections
Right arrow Statistics & Research Methods

Canadian Medical Association Journal, Vol 152, Issue 1 27-32, Copyright © 1995 by Canadian Medical Association

JOURNAL ARTICLE

Basic statistics for clinicians: 1. Hypothesis testing

G. Guyatt, R. Jaeschke, N. Heddle, D. Cook, H. Shannon and S. Walter
Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ont.

In the first of a series of four articles the authors explain the statistical concepts of hypothesis testing and p values. In many clinical trials investigators test a null hypothesis that there is no difference between a new treatment and a placebo or between two treatments. The result of a single experiment will almost always show some difference between the experimental and the control groups. Is the difference due to chance, or is it large enough to reject the null hypothesis and conclude that there is a true difference in treatment effects? Statistical tests yield a p value: the probability that the experiment would show a difference as great or greater than that observed if the null hypothesis were true. By convention, p values of less than 0.05 are considered statistically significant, and investigators conclude that there is a real difference. However, the smaller the sample size, the greater the chance of erroneously concluding that the experimental treatment does not differ from the control--in statistical terms, the power of the test may be inadequate. Tests of several outcomes from one set of data may lead to an erroneous conclusion that an outcome is significant if the joint probability of the outcomes is not taken into account. Hypothesis testing has limitations, which will be discussed in the next article in the series.


This article has been cited by other articles:


Home page
 Anesth. Analg.Home page
P. M. Jones and T. P. Turkstra
The Assessment of Sore Throat Following Nasotracheal Intubation
Anesth. Analg., April 1, 2009; 108(4): 1351 - 1352.
[Full Text] [PDF]

Home page
 J Biomol ScreenHome page
Z. Zhu, J. Puglisi, D. Connors, J. Stewart, J. Herbst, A. Marino, M. Sinz, J. O'Connell, M. Banks, K. Dickinson, et al.
Use of Cryopreserved Transiently Transfected Cells in High-Throughput Pregnane X Receptor Transactivation Assay
J Biomol Screen, March 1, 2007; 12(2): 248 - 254.
[Abstract] [PDF]

Home page
 J Am Board Fam MedHome page
J. P. Marasinghe and A. A. Amarasinghe
Will This Exercise Be Good Enough?
J Am Board Fam Med, January 1, 2007; 20(1): 98 - 99.
[Full Text] [PDF]

Home page
 JBJSHome page
H. V. Lochner, M. Bhandari, and P. Tornetta III
Type-II Error Rates (Beta Errors) of Randomized Trials in Orthopaedic Trauma
J. Bone Joint Surg. Am., November 1, 2001; 83(11): 1650 - 1655.
[Abstract] [Full Text] [PDF]