BMJ  2003;327:752 (27 September), doi:10.1136/bmj.327.7417.752-a

Letter

Statistical interpretation can also bias research evidence

The first 150 words of the full text of this article appear below.

EDITOR—Kaptchuk discussed the effect of interpretive bias on research evidence.¹ Let me add one more example. Studies are designed to determine whether "a statistically significant difference" exists between the outcomes of two alternative treatments. If no difference is discovered the temptation for authors is to conclude that the treatment under investigation is "just as good" as the gold standard. To make such a statement, the study needs to have adequate statistical power, ensuring the chance of a type II error (incorrectly accepting the null hypothesis) is sufficiently small.

Since power can generally be increased by enlarging the sample size, it has become popular for researchers who do not have sufficient power to speculate in a way that makes the actual power meaningless. For example, such a typical speculative statement might read: "While the study failed to have sufficient power to confirm the findings that the drugs were not . . . [Full text of this article]

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Effect of interpretive bias on research evidence

Ted J Kaptchuk
BMJ 2003 326: 1453-1455. [Extract] [Full Text] [PDF]