RT Book, Section
A1 Glantz, Stanton A.
SR Print(0)
ID 57415371
T1 Chapter 7. Confidence Intervals
T2 Primer of Biostatistics, 7e
YR 2012
FD 2012
PB The McGraw-Hill Companies
PP New York, NY
SN 978-0-07-178150-3
LK accessbiomedicalscience.mhmedical.com/content.aspx?aid=57415371
RD 2021/09/22
AB All the statistical procedures developed so far were designed to help decide whether or not a set of observations is compatible with some hypothesis. These procedures yielded P values to estimate the chance of reporting that a treatment has an effect when it really does not and the power to estimate the chance that the test would detect a treatment effect of some specified size. This decision-making paradigm does not characterize the size of the difference or illuminate results that may not be statistically significant (i.e., not associated with a value of P below .05) but does nevertheless suggest an effect. In addition, since P depends not only on the magnitude of the treatment effect but also the sample size, it is not unusual for experiments with large sample sizes to yield very small values of P (what investigators often call “highly significant” results) when the magnitude of the treatment effect is so small that it is clinically or scientifically unimportant. As Chapter 6 noted, it can be more informative to think not only in terms of the accept—reject approach of statistical hypothesis testing but also to estimate the size of the treatment effect together with some measure of the uncertainty in that estimate.