Bayes Offers a 'New' Way to Make Sense of Numbers

Bayesian statistics, which allows researchers to use everything from hunches to hard data to compute the probability that a hypothesis is correct (see p. 1461), is experiencing a renaissance in fields of science ranging from astrophysics to genomics and in real-world applications such as testing new drugs and setting catch limits for fish. Advances in computers and the limitations of traditional statistical methods are part of the reason for the new popularity of this approach, first proposed in a 1763 paper by the Reverend Thomas Bayes. In addition, advocates say it produces answers that are easier to understand and forces users to be explicit about biases obscured by reigning frequentist approaches. Detractors, on the other hand, fear that because Bayesian analysis can take into account prior opinion, it could spawn less objective evaluations of experimental results.