spotify_confidence/analysis/abstract_base_classes/confidence_abc.py [74:114]:
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        absolute: bool,
        groupby: Union[str, Iterable],
        non_inferiority_margins: NIM_TYPE,
        final_expected_sample_size_column: str,
        verbose: bool,
        minimum_detectable_effects_column: str,
    ) -> DataFrame:
        """Args:
            groupby (str): Name of column.
                If specified, will plot a separate chart for each level of the
                grouping.
            non_inferiority_margins (Union[Tuple[float, str], Dict[str, Tuple[float, str]], bool]):
                Pass tuple(non_inferiority_margin, preferred direction) to use the same NIM for all
                comparisons, e.g. (0.01, 'increase'), which means that we want
                level_2 to be grater than the average of level_1 times (1-0.01),
                or (0.05, 'decrease') which means that we want
                level_2 to be smaller than the average
                of level_1 times (1+0.01).
                Pass dictionary {{group:tuple(non_inferiority_margin, preferred direction}} to use
                different non-inferiority margins for different values of
                groupby column.
                To performe a one-sided test without nim, use
                (None, preffered direction).
                Alternatively, pass True to use the "non_inferiority_margin" and "preferred_direction"
                columns of dataframe that was passed to the contructor, as source of nims.
            final_expected_sample_size_column (str): Column in source data frame containing expected number of
                    observations at end of experiment.
                Use in combination with ordinal groupby to perform a
                sequential test. See https://cran.r-project.org/web/packages/ldbounds/index.html for details.
            verbose (bool): include columns used in intermediate steps in the calculations in returned dataframe.
            minimum_detectable_effects_column (str): The minimum detectable effect, used for calculating required
            sample size.

        Returns:
            Dataframe containing the difference in means between
            group 1 and 2, p-values and confidence intervals for each value
            in the groupby column
        """
        pass

    @abstractmethod
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spotify_confidence/analysis/abstract_base_classes/confidence_abc.py [118:159]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        absolute: bool,
        groupby: Union[str, Iterable],
        non_inferiority_margins: NIM_TYPE,
        final_expected_sample_size_column: str,
        verbose: bool,
        minimum_detectable_effects_column: str,
    ) -> DataFrame:
        """Args:
            levels: (list(tuple)): list of levels to compare
            groupby (str): Name of column.
                If specified, will plot a separate chart for each level of the
                grouping.
            non_inferiority_margins (Union[Tuple[float, str], Dict[str, Tuple[float, str]], bool]):
                Pass tuple(non_inferiority_margin, preferred direction) to use the same NIM for all
                comparisons, e.g. (0.01, 'increase'), which means that we want
                level_2 to be grater than the average of level_1 times (1-0.01),
                or (0.05, 'decrease') which means that we want
                level_2 to be smaller than the average
                of level_1 times (1+0.01).
                Pass dictionary {{group:tuple(non_inferiority_margin, preferred direction}} to use
                different non-inferiority margins for different values of
                groupby column.
                To performe a one-sided test without nim, use
                (None, preffered direction).
                Alternatively, pass True to use the "non_inferiority_margin" and "preferred_direction"
                columns of dataframe that was passed to the contructor, as source of nims.
            final_expected_sample_size_column (str): Column in source data frame containing expected number of
                    observations at end of experiment.
                Use in combination with ordinal groupby to perform a
                sequential test. See https://cran.r-project.org/web/packages/ldbounds/index.html for details.
            verbose (bool): include columns used in intermediate steps in the calculations in returned dataframe.
            minimum_detectable_effects_column (str): The minimum detectable effect, used for calculating required
            sample size.

        Returns:
            Dataframe containing the difference in means between
            group 1 and 2, p-values and confidence intervals for each value
            in the groupby column
        """
        pass

    @abstractmethod
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