'Returns the confidence interval of the value, `effect` of the constraint.
This is currently only available for t and z tests.
Parameters
alpha : float, optional
The significance level for the confidence interval.
ie., The default `alpha` = .05 returns a 95% confidence interval.
Returns
ci : ndarray, (k_constraints, 2)
The array has the lower and the upper limit of the confidence
interval in the columns.'

def conf_int(self, alpha=0.05):

    if (self.effect is not None):
        q = self.dist.ppf((1 - (alpha / 2.0)), *self.dist_args)
        lower = (self.effect - (q * self.sd))
        upper = (self.effect + (q * self.sd))
        return np.column_stack((lower, upper))
    else:
        raise NotImplementedError('Confidence    Interval    not    available')
