Statistical-Decision Theory [SDT] Law and Legal Definition
Statistical decision theory or SDT is a method for determining whether a panel of potential jurors was selected from a fair cross section of the community. It calculates probabilities and measures the likelihood that under representation could have occurred by sheer chance. Under this method, if one can determine that it is statistically improbable that the jury pool resulted from random selection, then there is an imperfection in the jury selection system. This method has been criticized because a pool of potential jurors is not ordinarily selected by mere chance. Potential jurors are disqualified for many legitimate reasons. Indeed, no court in the U.S. has accepted SDT alone as determinative in Sixth Amendment challenges to jury selection systems.
The following is an example of a case law on statistical decision theory:
The intellectual core of SDT is random selection. SDT measures the probability that the selection of a particular class of jurors (e.g., blue-eyed, blonde men) is random. In the jury context, the greater the chance of randomness, the "better" the jury selection system. But if the sample is not random (e.g., all Scandinavians are excluded from the sample), SDT will produce a skewed probability prediction. It is illogical to apply a theory based on random selection when assessing the constitutionality of a qualified wheel. By definition, the qualified wheel is not the product of random selection; it entails reasoned disqualifications based on numerous factors. It is irrational to gauge the qualified wheel an inherently non-random sample by its potential for randomness. [United States v. Rioux, 97 F.3d 648, 655 (2d Cir. Conn. 1996)]
