def METHOD_NAME(self):
    df = pd.DataFrame(
        [
            ['1', '1', 1],
            ['1', '2', 997],
            ['1', '3', 998],
            ['1', '4', 999],
            ['1', '5', 1000],
            ['1', '6', 997],
            ['1', '7', 998],
            ['1', '8', 999],
            ['1', '9', 1000],
            ['1', '10', 997],
            ['1', '11', 998],
            ['1', '12', 999],
        ],
        columns=['number1.', 'num,ber2', 'number ([3])'],
    )
    column_types = {
        'number1.': 'text',
        'num,ber2': 'category',
        'number ([3])': 'number',
    }
    statistics = {
        'number ([3])/average': 915.25,
        'number ([3])/outlier_count': 1,
        'number ([3])/std': 287.92,
    }
    result = RemoveOutliers(df, column_types, statistics).evaluate()
    self.assertEqual(
        result,
        [
            dict(
                title='Remove outliers',
                message='Remove 1 outlier(s) and null values to reduce the amount of noise '
                        'in this column.',
                status='not_applied',
                action_payload=dict(
                    action_type='filter',
                    action_arguments=['number ([3])'],
                    action_code='"number ([3])" <= 1779.01 and "number ([3])" >= '
                                '51.49000000000001',
                    action_options={},
                    action_variables={},
                    axis='row',
                    outputs=[],
                ),
            )
        ],
    )