![]() ![]() ![]() For example, some people like to complain and will, if asked, complain both about life’s experiences (such as stress) and also subjective health outcomes (such as having chest pain). Selection bias could produce a study database in which a given exposure is related to a variety of characteristics that increase (or decrease) risk of disease, where such associations are not apparent in the general population. Selection and information biases also need to be considered. One solution here is to be much more stringent with “significance” levels, moving to P<0.001 or beyond, rather than P<0.05. w7 These false positive findings are the true products of data dredging, resulting from simply looking at too many possible associations. When a large number of associations can be looked at in a dataset where only a few real associations exist, a P value of 0.05 is compatible with the large majority of findings still being false positives. w6 The misinterpretation of a P<0.05 significance test as meaning that such findings will be spurious on only 1 in 20 occasions unfortunately continues. Data dredging is thought by some to be the major problem: epidemiologists have studies with a huge number of variables and can relate them to a large number of outcomes, with one in 20 of the associations examined being “statistically significant” and thus acceptable for publication in medical journals. ![]() It would seem wiser to attempt a better diagnosis of the problem before prescribing Le Fanu’s solution. ![]()
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