PT  - JOURNAL ARTICLE
AU  - G. Guyatt
AU  - R. Jaeschke
AU  - N. Heddle
AU  - D. Cook
AU  - H. Shannon
AU  - S. Walter
TI  - Basic statistics for clinicians: 1. Hypothesis testing
DP  - 1995 Jan 01
TA  - Canadian Medical Association Journal
PG  - 27--32
VI  - 152
IP  - 1
4099  - http://www.cmaj.ca/content/152/1/27.short
4100  - http://www.cmaj.ca/content/152/1/27.full
SO  - CMAJ1995 Jan 01; 152
AB  - 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.