Abstract
AbstractHypothesis tests are routinely misinterpreted in scientific research. Specifically, the failure to reject a null hypothesis is often interpreted as support for the null hypothesis while the rejection of a null hypothesis is often interpreted as evidence of an important finding. Many of the most frequently used hypothesis tests are “non‐informative” because the null hypothesis is known to be false prior to hypothesis testing. We discuss the limitations of non‐informative hypothesis tests and explain why confidence intervals should be used in their place. Several examples illustrate the use and interpretation of confidence intervals. Copyright © 2007 John Wiley & Sons, Ltd.
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