AbstractID: 6905 Title: A Bayesian approach to the problem of calculating the confidence limits of the probability of success in animal experiments and clinical studies Experiments in which outcomes of only two types can be observed are subject to binomial statistics. Clinical studies and animal experiments in which the treatment results are classified just as positive (success) or negative (failure) outcomes are typical examples of such experiments. In these studies, the unknown parameter is the probability of success, p. In this paper a bayesian approach is proposed to the problem of defining and calculating the confidence limits of p. The bayesian method is based on the construction of a distribution function of p depending on the observed number of successes, k and number of trials, n. An analytical expression of the distribution function is presented. On its basis the most probable value of p together with its mean, variance and confidence limits are calculated applying the usual definitions of these characteristics of a random variable with a known distribution. A comparison with the confidence limits values calculated using the normal approximation to the binomial distribution is done. A perfect convergence is shown to exist in the range of validity of the normal approximation. At the same time the commonly used confidence limits formulae show no convergence to the normal approximation values. The bayesian method works for any number of trials (large and small) and all possible values of number of successes, including k=0 and k=n, providing exact formulae for the calculation of the confidence limits in all cases. A Matlab code for the calculation of the confidence limits according to the proposed method is provided.