  Textbooks on statistics emphasize care and precision, via concepts such as
reliability and validity in measurement, random sampling and treatment
assignment in data collection, and causal identification and bias in
estimation. But how do researchers decide what to believe and what to trust
when choosing which statistical methods to use? How do they decide the
credibility of methods? Statisticians and statistical practitioners seem to
rely on a sense of anecdotal evidence based on personal experience and on the
attitudes of trusted colleagues. Authorship, reputation, and past experience
are thus central to decisions about statistical procedures.
