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Why do different epidemiological studies come to different conclusions?

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Why do different epidemiological studies come to different conclusions?

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Uncertainty is a fact of life common to all areas of science. Experiments done in the laboratory can usually be designed and controlled so that the uncertainty is minimised and the results can be regarded with confidence. With epidemiology, however, we are dealing with people in the real world and things are not that easy. When looking for possible effects of radiation, the fundamental statistical limitation is determined by (a) the numbers of people exposed to significant doses and (b) the numbers of cases of cancer, mutations, etc. The power of the study is a statistical estimate of the smallest effect that could be expected to be detected with confidence. If the numbers of cases or number of people are too small, even in a well-designed study, there is a high probability that chance will lead to the wrong conclusion and the study cannot be relied upon. Furthermore, almost all epidemiological studies are subject to bias in their design, ie, it is not always possible to find a group o

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