Why Bother with Statistics?
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Many statistical practices commonly used by medical researchers, including both statisticians and non-statisticians, have severe flaws that often are not obvious. This chapter begins with a brief list of some of the examples that will be covered in greater detail in later chapters. The point is made, and illustrated repeatedly, that what may seem to be a straightforward application of an elementary statistical procedure may have one or more problems that are likely to lead to incorrect conclusions. Such problems may arise from numerous sources, including misapplication of a method that is not valid in a particular setting, misinterpretation of numerical results, or use of a conventional statistical procedure that is fundamentally wrong. Examples will include being misled by The Innocent Bystander Effect when determining causality, how conditional probabilities may be misinterpreted, the relationship between gambling and medical decision-making, and the use of Bayes’ Law to interpret the results of a test for a disease or to compute the probability that a child will have hemophilia based on what has been observed in family members.