src/mozanalysis/bayesian_stats/bayesian_bootstrap.py [190:233]:
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    num_samples=10000,
    seed_start=None,
    threshold_quantile=None,
    summary_quantiles=mabs.DEFAULT_QUANTILES,
):
    """Bootstrap ``stat_fn`` for one branch on its own.

    Computes ``stat_fn`` for ``num_samples`` resamples of ``data``,
    then returns summary statistics for the results.

    Args:
        data: The data as a list, 1D numpy array, or pandas Series
        stat_fn (callable, optional): A function that either:

            * Aggregates each resampled population to a scalar (e.g.
              the default, ``bb_mean``), or
            * Aggregates each resampled population to a dict of
              scalars (e.g. the func returned by
              ``make_bb_quantile_closure`` when given multiple
              quantiles.

            In both cases, this function must accept two parameters:

            * a one-dimensional ndarray or pandas Series of values,
            * an identically shaped object of weights for these values

        num_samples: The number of bootstrap iterations to perform
        seed_start: An int with which to seed numpy's RNG. It must
            be unique within this set of calculations.
        threshold_quantile (float, optional): An optional threshold
            quantile, above which to discard outliers. E.g. ``0.9999``.
        summary_quantiles (list, optional): Quantiles to determine the
            confidence bands on the branch statistics. Change these
            when making Bonferroni corrections.
    """
    samples = get_bootstrap_samples(
        data, stat_fn, num_samples, seed_start, threshold_quantile
    )

    return mabs.summarize_one_branch_samples(samples, summary_quantiles)


def get_bootstrap_samples(
    data,
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src/mozanalysis/frequentist_stats/bootstrap.py [106:142]:
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    num_samples=10000,
    seed_start=None,
    threshold_quantile=None,
    summary_quantiles=mabs.DEFAULT_QUANTILES,
):
    """Run a bootstrap for one branch on its own.

    Resamples the data ``num_samples`` times, computes ``stat_fn`` for
    each sample, then returns summary statistics for the distribution
    of the outputs of ``stat_fn``.

    Args:
        data: The data as a 1D numpy array, pandas series, or pandas dataframe.
        stat_fn: Either a function that aggregates each resampled
            population to a scalar (e.g. the default value ``np.mean``
            lets you bootstrap means), or a function that aggregates
            each resampled population to a dict of scalars. In both
            cases, this function must accept a one-dimensional ndarray
            as its input.
        num_samples: The number of bootstrap iterations to perform
        seed_start: An int with which to seed numpy's RNG. It must
            be unique within this set of calculations.
        threshold_quantile (float, optional): An optional threshold
            quantile, above which to discard outliers. E.g. ``0.9999``.
        summary_quantiles (list, optional): Quantiles to determine the
            confidence bands on the branch statistics. Change these
            when making Bonferroni corrections.
    """
    samples = get_bootstrap_samples(
        data, stat_fn, num_samples, seed_start, threshold_quantile
    )

    return mabs.summarize_one_branch_samples(samples, summary_quantiles)


def get_bootstrap_samples(
    data,
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