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Given N data points drawn from a chi-square distribution, we use Bayesianinference to determine most likely values and N-dependent confidence intervalsfor the width sigma and the number k of degrees of freedom of thatdistribution. Using reduced partial neutron widths measured in a number ofnuclei, a guessed value of sigma, and a maximum-likelihood approach (differentfrom Bayesian inference), Koehler et al. and Koehler have determined the mostlikely k-values of chi-square distributions that fit the data. In all casesthey find values for k that differ substantially from k = 1 (the valuecharacterizing the Porter-Thomas distribution (PTD) predicted by random-matrixtheory). The authors conclude that the validity of the PTD must be rejectedwith considerable statistical significance. We show that the value of sigmaguessed in these papers lies far outside the Bayesian confidence interval forsigma, casting serious doubt on the results of and the conclusions drawn there.We also show that sigma and k must both be determined from the data. Comparisonof the results with the Bayesian confidence intervals would then decide onacceptance or rejection of the PTD.

Tests of the Porter-Thomas Distribution for Reduced Partial Neutron Widths