code: def confidence_interval(self, alpha=0.9):
          
          m, _, _ = scipy.stats.bayes_mvs(
              [r[self.metric_name_] for _, r in self.results_], alpha=alpha)
          return m
docs: Compute confidence interval on accumulated metric values
  
          Parameters
          ----------
          alpha : float, optional
              Probability that the returned confidence interval contains
              the true metric value.
  
          Returns
          -------
          (center, (lower, upper))
              with center the mean of the conditional pdf of the metric value
              and (lower, upper) is a confidence interval centered on the median,
              containing the estimate to a probability alpha.
  
          See Also:
          ---------
          scipy.stats.bayes_mvs