During the last decade, evidence-based medicine has been described as a paradigm shift in clinical practice, and as "the conscientious, explicit, and judicious use of current best evidence in making decisions about the care of individual patients". Appropriate statistical methods for analyzing data are critical for the correct interpretation of the results in proof of the evidence. However, in the medical literature, these statistical methods are often incorrectly interpreted or misinterpreted, leading to serious methodological errors and misinterpretations. This review highlights several important aspects related to the design and statistical analysis for evidence-based reproductive medicine. First, we clarify the distinction between ratios, proportions, and rates, and then provide a definition of pregnancy rate. Second, we focus on a special type of bias called 'confounding bias', which occurs when a factor is associated with both the exposure and the disease but is not part of the causal pathway. Finally, we present concerns regarding misuse of statistical software or application of inappropriate statistical methods, especially in medical research.
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