In science, in the law, in business, and in everyday social interaction, people attempt to base their beliefs on the available evidence. The standard (Bayesian) theory asserts that people's beliefs consist of probabilities for various propositions, for example, the probability that a particular scientific hypothesis is correct or the probability that an accused person is guilty as charged. But proposed applications of the Bayesian theory in science and in law have encountered strong and quite justifiable resistance, largely because the theory demands that people express detailed beliefs even in the absence of any evidence (prior probabilities). This research will develop non-Bayesian theories in which perceived strength of evidence will be measured on a numerical scale quite different from probability. This measurement system is well adapted to science and the law since, in the absence of evidence, all beliefs can be rated as zero. One main research activity will be to determine formulae for converting probabilistic and statistical information to the strength-of-evidence measurement scale, and thus will provide a basis for systematic integration of such information with other kinds of evidence. Another research activity will be to develop graded standards of statistical and nonstatistical evidence, against which arbitrary items of evidence can be compared and evaluated, much as a meterstick provides a graded series of lengths, against which the lengths of other objects can be measured. The graded standards for evidence will be most useful if they can be applied analytically, decomposing a complex body of evidence into parts, each of which can be evaluated by comparison with the standards. One of the principal research activities will be to evaluate the bias inherent in such analytic decompositions. That research will compare evaluations of partial evidence by two groups of people, those who are aware of the full body of evidence and those who are not. If possible, conditions will be found for which these two groups evaluate evidence identically, i.e., there is no appreciable bias due to parts of the evidence that are ignored in any step of the analytic process. The research will also test conditions under which strong biases are expected.
