The propensity to interpret data according to prior beliefs, confirmation bias is one of the most insidious forms of bias in research: old and modern examples are offered. Misinterpretation of study results is commonplace in the courtroom, often described under the rubric of “junk science.” The association of a rare exposure with a rare outcome is increasingly the focus of biomedical research, this incurs increased opportunity for bias to influence study results. Absolute rather than relative risks are an important form of interpreting rare study data. Reverse causality is a profound source of error: Is the disease responsible for increasing exposure to the putative risk factor? Various biases are linked with time: In the context of public health screening, there is lead time bias and length time bias; and for survival studies, immortal time bias. Stein’s paradox offers a caution that the results of a larger sample may actually be more predictive of the subgroup experience within that sample than the study result observed for that subgroup.