This book considers experiments whose data are analyzed through statistical tests. A significant outcome of a test is considered a success, whereas a non-significant one is a failure.

Data are supposed to be collected with a certain amount of randomness, which implies the adoption of statistical tests for data analysis. Consequently, also the outcomes of the tests, i.e. success/failure, are affected by randomness. So, the probability of a successful outcome in these experiments, i.e. the probability of a significant outcome, is of great interest to researchers, sponsors of research and users of research results.

Focus is placed on large experiments that have been preceded by pilot ones. A pilot experiment is often performed in order to achieve data for deciding whether or not to launch the successive, important study and, if this is the case, to adequately plan the latter.

One of the contexts in which the framework above can be found is that of clinical trials. Here, large experiments are phase III trials, and previous phase II studies can be considered pilot studies in view of the subsequent phase III studies. A brief introduction to clinical trials follows, together with some data on their success rates and an introduction to their individual probability of success.

To conclude, in order to introduce applied problems related to success probability estimation, and to motivate the latter, two practical situations ...

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