Heed the data when results are not significant.

When randomised controlled trials show a difference that is not statistically significant there is a risk of interpretive bias.1 Interpretive bias occurs when authors and readers overemphasise or underemphasise results. For example, authors may claim that the non-significant result is due to lack of power rather than lack of effect, using terms such as borderline significance or stating that no firm conclusions can be drawn because of the modest sample size. In contrast, if the study shows a non-significant effect that opposes the study hypothesis, it may be downplayed by emphasising the results are not statistically significant. We investigated the problem of interpretive bias in a sample of recently published trials with findings that did not support the study hypothesis. [ABSTRACT FROM AUTHOR]