def _compute_ci_for_row()

in spotify_confidence/analysis/frequentist/confidence_computers/z_test_computer.py [0:0]

• 19 lines of code
• 10 McCabe index (conditional complexity)

    def _compute_ci_for_row(row: Series) -> Tuple[float, float]:
        if row[IS_SIGNIFICANT] and num_significant == m_scal:
            alpha_adj = adjusted_alpha_rej_equal_m
        elif row[IS_SIGNIFICANT] and num_significant < m_scal:
            alpha_adj = adjusted_alpha_rej_less_m
        else:
            alpha_adj = adjusted_alpha_accept

        ci_sign = -1 if row[PREFERENCE_TEST] == "larger" else 1
        bound1 = row[DIFFERENCE] + ci_sign * st.norm.ppf(alpha_adj) * row[STD_ERR]
        if ci_sign == -1:
            bound2 = max(row[NULL_HYPOTHESIS], bound1)
        else:
            bound2 = min(row[NULL_HYPOTHESIS], bound1)

        bound = bound2 if row[IS_SIGNIFICANT] else bound1

        lower = bound if row[PREFERENCE_TEST] == "larger" else -np.inf
        upper = bound if row[PREFERENCE_TEST] == "smaller" else np.inf

        row[ADJUSTED_LOWER] = lower
        row[ADJUSTED_UPPER] = upper

        return row