src/mozanalysis/frequentist_stats/sample_size.py [426:461]:
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    df: pd.DataFrame,
    metrics_list: list[Metric],
    effect_size: float = 0.01,
    alpha: float = 0.05,
    power: float = 0.90,
    outlier_percentile: float = 99.5,
) -> SampleSizeResultsHolder:
    """
    Perform sample size calculation for an experiment to test for a
    difference in proportions.

    Args:
        df: A pandas DataFrame of queried historical data.
        metrics_list (list of mozanalysis.metrics.Metric): List of metrics
            used to construct the results df from HistoricalTarget. The names
            of these metrics are used to return results for sample size
            calculation for each
        effect_size (float, default .01): Difference in proportion for the
            minimum detectable effect --
            effect_size = p(event under alt) - p(event under null)
        alpha (float, default .05): Significance level for the experiment.
        power (float, default .90): Probability of detecting an effect,
            when a significant effect exists.
        outlier_percentile(float, default .995): Percentile at which to trim
            each columns.

    Returns:
        SampleSizeResultsHolder: The data attribute contains a dictionary.
        Keys in the dictionary are the metrics column names from
        the DataFrame; values are the required sample size per branch to achieve
        the desired power for that metric. Additional methods for ease of use
        are documented in the class.
    """

    def _get_sample_size_col(col):
        p = np.percentile(df[col], q=[outlier_percentile])[0]
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src/mozanalysis/frequentist_stats/sample_size.py [707:742]:
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    df: pd.DataFrame,
    metrics_list: list[Metric],
    effect_size: float = 0.01,
    alpha: float = 0.05,
    power: float = 0.90,
    outlier_percentile: float = 99.5,
) -> SampleSizeResultsHolder:
    """
    Sample size for test of difference of Poisson rates,
    based on Poisson rate's asymptotic normality.

    Args:
        df: A pandas DataFrame of queried historical data.
        metrics_list (list of mozanalysis.metrics.Metric): List of metrics
            used to construct the results df from HistoricalTarget. The names
            of these metrics are used to return results for sample size
            calculation for each
        test (str, default `z`): `z` or `t` to indicate which solver to use
        effect_size (float, default .01): Percent change in metrics
            expected as a result of the experiment treatment
        alpha (float, default .05): Significance level for the experiment.
        power (float, default .90): Probability of detecting an effect,
            when a significant effect exists.
        outlier_percentile(float, default .995): Percentile at which to trim
            each columns.

    Returns:
        SampleSizeResultsHolder: The data attribute contains a dictionary.
        Keys in the dictionary are the metrics column names from
        the DataFrame; values are the required sample size per branch to achieve
        the desired power for that metric.  Additional methods for ease of use
        are documented in the class
    """

    def _get_sample_size_col(col):
        p = np.percentile(df[col], q=[outlier_percentile])[0]
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