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A computational algorithm is developed for generating the cumulative distribution of successes in a sequence of dependent trials, characterized by a Markov model, for which tables of the distribution may be compiled. Such tables may be used to assess the impact of such dependence on the calculation of confidence intervals and the testing of hypotheses. Examples are presented to show how such confidence intervals and hypothesis tests deteriorate with increasing dependence.
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