Decision making is a crucial element in the field of medicine. The physician has to determine what is wrong with the patient and recommend treatment, while the patient has to decide whether or not to seek medical care, and go along with the treatment recommended by the physician. Health policy makers and health insurers have to decide what to promote, what to discourage, and what to pay for. Together, these decisions determine the quality of health care that is provided. Decision (...) Making in Health Care is an up-to-date, comprehensive overview of the field of medical decision making. It includes quantitative theoretical tools for modeling decisions, psychological research on how decisions are actually made, and applied research on how physician and patient decision making can be improved. (shrink)

When using sample data to decide whether two populations differ, laypeople attend to the difference between group means, but largely overlook within-group variability (Obrecht, Chapman, & Gelman, 2007). We show, first, that laypeople know about and use story-implied variability when making pairwise comparisons. Then we demonstrate that participants' sensitivity to variance in a dataset is boosted when presented in a context that implies consistent variance information. Statistical data were couched in stories about electrical conductivity measurements obtained from element samples (low-variability (...) category) or body weight measurements from samples of peoples (high-variability category). We manipulated, between participants, whether the data variance matched or mismatched the story-implied variability. Participants who received data in a matching context showed high sensitivity to variance, while those in the mismatching condition did not. Laypeople use statistical data to make reasonable inferences when those data are provided in a context that makes sense. (shrink)