Bootstrap mean and confidence intervals

classical_bootstrapping.py

def bootstrap_mean_and_ci(
    data: List[float], n_iter: int = 1000, alpha: float = 0.95
) -> Tuple[float, float, float]:
    n = len(data)

    bootstrap_samples = np.random.choice(data, (n_iter, n), replace=True)

    means = bootstrap_samples.mean(axis=1)

    mean = means.mean()
    lower = np.percentile(means, (1 - alpha) / 2 * 100)
    upper = np.percentile(means, (1 + alpha) / 2 * 100)

    return mean, lower, upper