For a candidate attenuation coefficient, the one-second expected count is . The model predictions are
Use first the conservative signal-dominated range , where the count approximation is reliable and background is a small correction. Using the estimated errors from part (c), the weighted residual statistic is
It gives . Since both candidates are fixed and was calibrated independently, neither was fitted to these four counts: the approximate reference has , not three. The upper-tail probabilities are approximately and , respectively. Thus is compatible with the scatter, whereas is disfavored at the usual level in this approximate test.
A Pearson chi-squared statistic, using model variances instead of estimated observed variances, gives the same preference:
Including the fifth point also leaves the decision unchanged: the estimated-error statistics are and on five degrees of freedom, with approximate upper-tail probabilities and ; the Pearson statistics are and . Hence
The small uncertainty in contributes correlated prediction uncertainty, as in chi-squared comparison of a calibrated attenuation model, but is negligible compared with the one-second counting errors here. Choosing the test range after inspecting the observations limits a strict interpretation of its nominal significance; the numerical comparison and its stated range are the relevant evidence.
If all eight observations are inserted into the estimated-error formula, the totals are and . Their nominal eight-degree-of-freedom calibration is unreliable because the final expected counts are much smaller than one for some predictions. In fact the full-range Pearson statistics are and , exposing the late excess much more strongly. The full Poisson deviance values are and , again favoring but signaling that a background-free exponential is inadequate over the complete range. These sparse-count comparisons should not be used to claim that either unmodified model explains all eight measurements.

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