Applied Modeling and Computations in Nuclear Science by Thomas M. Semkow, Stefaan Pommé, Simon Jerome, and Daniel J.

By Thomas M. Semkow, Stefaan Pommé, Simon Jerome, and Daniel J. Strom (Eds.)

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E. Neyman-Pearson confidence intervals for extreme low-level, paired counting. Health Phys. 1999, 76, 186-190. 26. ; Lewis, P. : London, 1966. 27. Hald, A . Statistical Theory with Engineering Applications; Chapman & Hall: London, 1952. 28. Shuie, W. K . ; Bain, L . J. Experiment size and power comparisons for 2sample Poisson tests. Appl. Stat. C 1982, 31, 130-134. 29. Irwin, J. O. The frequency distribution of the difference between two independent variâtes following the same Poisson distribution.

6 is the closest single decimal digit adjustment for n in the range n = 3 to 20. 50, rapidly obtains. 6) where n , n' are independent background observations - thus overcoming the most serious deficiency of Eq. 5, as usually applied. 00, was performed as an exploratory study. The results show a dramatic difference between the usual practice (Eq. 5) and the application of Eq. 6 as an approximate /-test (Figure 3). 002), where u = standard uncertainty. The empirical distribution in Figure 3a is clearly not normal, especially in the tails.

645V^u. B B B υ B L L Β υ This statement (p. 49 of Ref. (24)), has on occasion been misinterpreted as referring to the estimated (observed) number of background counts, as opposed to the expected (true mean) value, for use as σ . The difference is profound, given the uncertainty of μ , for n = 5 counts. 51 - Table III, in Ref. (9)). ; ACS Symposium Series; American Chemical Society: Washington, DC, 2006. Β Β 26 differences - such as non-negativity for the Poisson, and continuity for the normal.

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