surpyval.univariate.nonparametric.success_run.success_run(n, confidence=None, alpha=None)

Calculate the minimum success probability of a run of ‘n’ independent events for a given confidence level. Useful when you want to know, with a certain amount of confidence what the probability of success is to be higher than a certain value.

Parameters

  • n (int) – The number of independent events in the run.

  • confidence (float, optional) – The desired confidence level, a value between 0 and 1. Default is 0.95, which corresponds to a 95% confidence level.

  • alpha (float, optional) – The significance level, a value between 0 and 1. Only used if confidence is not specified. Default is None.

Returns

The minimum success probability of a run of ‘n’ independent trials for the given confidence level.

Return type

float

Example

>>> from surpyval import success_run
>>> success_run(10)
0.7411344491069477