# AB Testing Module
## Overview
The AB Testing Library is a comprehensive suite designed for analyzing and reporting A/B test experiments, featuring functions for statistical analysis, advanced modeling, and data visualization to transform experimental results into actionable insights.
## Features
### A/B Testing Function Documentation
#### Purpose
The `ab_test` function is designed to facilitate A/B testing and statistical analysis within a given dataset. This robust tool can handle both binary and continuous outcome data, performing appropriate statistical tests to determine if there are significant differences between groups.
#### Function Map
<img src="https://github.com/knowusuboaky/ab_testing_module/blob/main/README_files/figure-markdown/mermaid-figure-1.png?raw=true" width="1526" height="459" alt="Optional Alt Text">
#### Parameters
- `data`: Pandas DataFrame containing the dataset for analysis.
- `group_column`: String specifying the column in `data` that contains the group labels.
- `value_column`: String specifying the column with the values to analyze.
- `control_group`: (Optional) String specifying the control group's label for comparison.
- `alpha`: (Optional) Float defining the significance level for the statistical tests, defaulting to 0.05.
- `handle_outliers`: (Optional) List specifying the method and strategy for handling outliers.
- `mc_correction`: (Optional) String specifying the method for multiple comparisons correction.
#### Functionality
- Validates input data and columns.
- Handles outliers if specified.
- Selects and performs the appropriate statistical tests based on data characteristics.
- Calculates effect sizes and conducts power analysis for the tests conducted.
- Applies multiple comparisons correction if specified.
- Generates interpretations of test results.
#### Output
Returns a Pandas DataFrame with columns for each test performed, including the test name, p-values, effect sizes, power, and interpretations.
#### Installation
This is the environment we need to load.
``` bash
pip install ab_testing_module==3.1.7
```
#### Load Package
``` bash
from ab_testing_module import ab_test
```
#### Base Operations
``` bash
# Perform ab test or abc test
df_results = ab_test(data=df,
group_column='group',
value_column='outcome',
control_group='control',
alpha=0.05,
handle_outliers=None,
mc_correction=None)
```
The `ab_test` function performs statistical analysis to compare outcomes across different groups in an A/B testing framework.
- **`data`**: The dataset to analyze, provided as a Pandas DataFrame. It should contain at least two columns: one for grouping (e.g., experimental vs. control groups) and one for the outcomes of interest (e.g., conversion rates, scores).
- **`group_column`**: A string specifying the name of the column in `data` that contains the group labels. This column is used to differentiate between the groups involved in the A/B test (e.g., 'group').
- **`value_column`**: A string indicating the name of the column in `data` that contains the values or outcomes to be analyzed (e.g., 'outcome'). These values are the focus of the statistical comparison between groups.
- **`control_group`**: (Optional) A string specifying the label of the control group within the `group_column`. This argument is essential when the analysis involves comparing each test group against a common control group to assess the effect of different treatments or conditions.
- **`alpha`**: (Optional) A float representing the significance level used in the statistical tests. The default value is `0.05`, which is a common threshold for determining statistical significance. Lowering this value makes the criteria for significance stricter, while increasing it makes the criteria more lenient.
- **`handle_outliers`**: (Optional) A list specifying the method and strategy for handling outliers in the `value_column`. The first element of the list is the method ('IQR' for interquartile range or 'Z-score'), and the second element is the strategy ('remove', 'impute', or 'cap'). If `None`, no outlier handling is performed.
- `IQR`: Identifies outliers based on the interquartile range. Typically, values below Q1-1.5*IQR or above Q3+1.5*IQR are considered outliers.
- `Z-score`: Identifies outliers based on the Z-score, with values typically beyond 3 standard deviations from the mean considered outliers.
- `remove`: Outliers are removed from the dataset.
- `impute`: Outliers are replaced with a specified statistic (e.g., the median).
- `cap`: Outliers are capped at a specified maximum and/or minimum value.
- **`mc_correction`**: (Optional) A string specifying the method for multiple comparisons correction when conducting multiple statistical tests. This argument is critical for controlling the family-wise error rate or the false discovery rate in experiments involving multiple group comparisons. Options include:
- `None`: No correction is applied.
- `'bonferroni'`: The Bonferroni correction, which adjusts p-values by multiplying them by the number of comparisons.
- `'fdr'`: The False Discovery Rate correction, which controls the expected proportion of incorrectly rejected null hypotheses.
- `'holm'`: The Holm-Bonferroni method, a step-down procedure that is less conservative than the standard Bonferroni correction.
- The function returns a Pandas DataFrame (`df_results`) containing the results of the statistical tests, including test names, p-values, effect sizes, power analyses (where applicable), and detailed interpretations of the results. The DataFrame provides a comprehensive summary of the findings from the A/B test analysis.
### Advanced Modeling Function Documentation
#### Purpose
The `modeling` is a sophisticated function designed for conducting advanced statistical modeling. It fits different types of statistical models to explore relationships between variables, quantify effects, and provide insights into the data structure.
#### Function Map
<img src="https://github.com/knowusuboaky/ab_testing_module/blob/main/README_files/figure-markdown/mermaid-figure-2.png?raw=true" width="1526" height="459" alt="Optional Alt Text">
#### Parameters
- `data`: Pandas DataFrame containing the dataset.
- `group_column`: String specifying the column with the group information.
- `value_column`: String specifying the column with the dependent variable.
- `control_group`: (Optional) String specifying the label of the control group.
#### Functionality
- Automatically determines the model type based on the dependent variable's characteristics.
- Fits the chosen model and generates summaries, including parameter estimates and their significance.
- Performs effect size and power analysis where applicable.
- Provides detailed interpretations of the modeling results.
#### Output
- Model summaries for each fitted model.
- Structured interpretations of each model's results.
- Effect sizes and results of power analyses for the conducted models.
#### Installation
This is the environment we need to load.
``` bash
pip install ab_testing_module==3.1.7
```
#### Load Package
``` bash
from ab_testing_module import modeling
```
#### Base Operations
``` bash
# Perform advanced modeling
model_summaries, interpretations, model_summaries_df, interpretations_df = modeling(data=df,
group_column='group',
value_column='outcome',
control_group='control')
```
The `modeling` function conducts advanced statistical modeling to explore relationships between variables in the context of A/B testing.
- **`data`** (DataFrame): The dataset for modeling, including independent variables and the dependent variable of interest.
- **`group_column`** (str): The column in `data` that identifies the grouping of data points (e.g., different conditions or treatments).
- **`value_column`** (str): The column in `data` representing the dependent variable or outcome of interest.
- **`control_group`** (str, optional): Specifies the label of the control group for comparative modeling purposes.
The function returns a tuple containing four elements, designed to provide both the raw statistical outputs from the modeling process and their synthesized interpretations for easier understanding:
- **`model_summaries`**: A list or other collection type that includes detailed summaries for each of the statistical models fitted during the analysis. These summaries typically contain information on model coefficients, p-values, confidence intervals, and other pertinent statistical metrics.
- **`interpretations`**: This output consists of narrative interpretations for each fitted model. It aims to translate the technical statistical findings into more accessible insights, highlighting significant results and their potential implications.
- **`model_summaries_df`** (`Pandas DataFrame`): A DataFrame consolidating the statistical summaries of all models into a tabular format. This structure facilitates a straightforward comparison across models, offering a clear view of the estimated effects and their statistical significance.
- **`interpretations_df`** (`Pandas DataFrame`): Similar to `model_summaries_df`, this DataFrame organizes the interpretations of each model's results into a structured table. It provides a narrative summary of the findings in an easily digestible format, suitable for reporting or further discussion.
### Data Visualization Function Documentation
#### Purpose
The `data_viz` function creates a series of data visualizations based on a provided dataset. It aims to facilitate data exploration, presentation, and the comprehension of statistical analyses through visual means.
#### Function Map
<img src="https://github.com/knowusuboaky/ab_testing_module/blob/main/README_files/figure-markdown/mermaid-figure-3.png?raw=true" width="1526" height="459" alt="Optional Alt Text">
#### Parameters
- `data`: Pandas DataFrame with the dataset to be visualized.
- `group_column`: String specifying the column with group labels.
- `value_column`: String specifying the column with values to analyze.
- `viz_types`: List of strings indicating the types of visualizations to generate.
#### Functionality
- Selects and customizes visualizations based on `viz_types`.
- Handles grouping and value considerations for data segmentation.
- Generates data-driven interpretations for each visualization.
- Employs interactive and aesthetically pleasing visualization techniques.
#### Output
- Displays requested visualizations.
- Provides interpretations for each visualization to highlight key findings.
#### Installation
This is the environment we need to load.
``` bash
pip install ab_testing_module==3.1.7
```
#### Load Package
``` bash
from ab_testing_module import data_viz
```
#### Base Operations
``` bash
# Create data visualization
visualizations = data_viz(data=df,
group_column='group',
value_column='outcome',
viz_types=['boxplot', 'violinplot', 'histogram', 'countplot', 'heatmap'])
```
The `data_viz` function creates a series of visualizations to aid in the exploratory data analysis and presentation of findings from the dataset.
- **`data`** (DataFrame): The dataset to be visualized, should include the variable(s) for grouping and the variable of interest.
- **`group_column`** (str): The column name in `data` that contains the labels for different groups or categories within the data.
- **`value_column`** (str): The column name in `data` that contains the values or outcomes to be visualized and analyzed.
- **`viz_types`** (list of str): A list specifying the types of visualizations to generate, such as `['boxplot', 'violinplot', 'histogram', 'countplot', 'heatmap']`.
## Ideal Use Cases
The Statistical Analysis Toolbox is designed to support a wide range of data analysis scenarios, particularly those involving A/B testing, statistical modeling, and data visualization. Below are the ideal use cases for each of the key functions within the toolbox: `ab_test`, `modeling`, and `data_viz`.
### `ab_test` Function
#### Ideal Use Cases
- **Comparing Conversion Rates**: Ideal for analyzing the effectiveness of two different webpage designs on user conversion rates. The `ab_test` function can statistically determine if one design leads to significantly higher conversions than the other.
- **Evaluating Marketing Strategies**: Useful for assessing the impact of different marketing campaigns or strategies on sales or customer engagement metrics.
- **Product Feature Testing**: When introducing new features or changes to a product, `ab_test` can help evaluate the change's impact on user behavior or satisfaction.
### `modeling` Function
#### Ideal Use Cases
- **Predictive Modeling**: For scenarios where the goal is to predict outcomes based on a set of variables, such as forecasting sales based on historical data and market conditions.
- **Causal Inference**: In situations where understanding the causal relationship between variables is crucial, such as determining the effect of educational interventions on student performance.
- **Complex Comparative Analysis**: Suitable for analyzing datasets with multiple groups and variables, where simple statistical tests are insufficient. For example, comparing the effects of various teaching methods across different schools or demographic groups.
### `data_viz` Function
#### Ideal Use Cases
- **Data Exploration**: Before diving into complex analyses, `data_viz` can help uncover patterns, trends, and outliers in the data, guiding further investigation.
- **Reporting and Presentation**: Creating compelling visual representations of analysis results for reports, presentations, or dashboards. It's particularly useful for conveying findings to non-technical stakeholders.
- **Comparative Analysis Visualization**: When the goal is to visually compare outcomes across different groups, such as visualizing the distribution of customer satisfaction ratings before and after implementing a customer service improvement plan.
## Contributing
We welcome contributions, suggestions, and feedback to make this library
even better. Feel free to fork the repository, submit pull requests, or
open issues.
## Documentation & Examples
For documentation and usage examples, visit the GitHub repository:
https://github.com/knowusuboaky/ab_testing_module
**Author**: Kwadwo Daddy Nyame Owusu - Boakye\
**Email**: kwadwo.owusuboakye@outlook.com\
**License**: MIT
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"description": "# AB Testing Module\r\n\r\n## Overview\r\n\r\nThe AB Testing Library is a comprehensive suite designed for analyzing and reporting A/B test experiments, featuring functions for statistical analysis, advanced modeling, and data visualization to transform experimental results into actionable insights.\r\n\r\n\r\n## Features\r\n\r\n### A/B Testing Function Documentation\r\n\r\n#### Purpose\r\n\r\nThe `ab_test` function is designed to facilitate A/B testing and statistical analysis within a given dataset. This robust tool can handle both binary and continuous outcome data, performing appropriate statistical tests to determine if there are significant differences between groups.\r\n\r\n#### Function Map\r\n\r\n<img src=\"https://github.com/knowusuboaky/ab_testing_module/blob/main/README_files/figure-markdown/mermaid-figure-1.png?raw=true\" width=\"1526\" height=\"459\" alt=\"Optional Alt Text\">\r\n\r\n#### Parameters\r\n\r\n- `data`: Pandas DataFrame containing the dataset for analysis.\r\n- `group_column`: String specifying the column in `data` that contains the group labels.\r\n- `value_column`: String specifying the column with the values to analyze.\r\n- `control_group`: (Optional) String specifying the control group's label for comparison.\r\n- `alpha`: (Optional) Float defining the significance level for the statistical tests, defaulting to 0.05.\r\n- `handle_outliers`: (Optional) List specifying the method and strategy for handling outliers.\r\n- `mc_correction`: (Optional) String specifying the method for multiple comparisons correction.\r\n\r\n#### Functionality\r\n\r\n- Validates input data and columns.\r\n- Handles outliers if specified.\r\n- Selects and performs the appropriate statistical tests based on data characteristics.\r\n- Calculates effect sizes and conducts power analysis for the tests conducted.\r\n- Applies multiple comparisons correction if specified.\r\n- Generates interpretations of test results.\r\n\r\n#### Output\r\n\r\nReturns a Pandas DataFrame with columns for each test performed, including the test name, p-values, effect sizes, power, and interpretations.\r\n\r\n\r\n#### Installation\r\n\r\nThis is the environment we need to load.\r\n\r\n``` bash\r\n\r\npip install ab_testing_module==3.1.7\r\n```\r\n\r\n#### Load Package\r\n\r\n``` bash\r\n\r\nfrom ab_testing_module import ab_test\r\n```\r\n\r\n#### Base Operations\r\n\r\n``` bash\r\n\r\n# Perform ab test or abc test\r\ndf_results = ab_test(data=df, \r\n group_column='group', \r\n value_column='outcome', \r\n control_group='control', \r\n alpha=0.05, \r\n handle_outliers=None, \r\n mc_correction=None)\r\n```\r\n\r\n\r\nThe `ab_test` function performs statistical analysis to compare outcomes across different groups in an A/B testing framework.\r\n\r\n\r\n- **`data`**: The dataset to analyze, provided as a Pandas DataFrame. It should contain at least two columns: one for grouping (e.g., experimental vs. control groups) and one for the outcomes of interest (e.g., conversion rates, scores).\r\n\r\n- **`group_column`**: A string specifying the name of the column in `data` that contains the group labels. This column is used to differentiate between the groups involved in the A/B test (e.g., 'group').\r\n\r\n- **`value_column`**: A string indicating the name of the column in `data` that contains the values or outcomes to be analyzed (e.g., 'outcome'). These values are the focus of the statistical comparison between groups.\r\n\r\n- **`control_group`**: (Optional) A string specifying the label of the control group within the `group_column`. This argument is essential when the analysis involves comparing each test group against a common control group to assess the effect of different treatments or conditions.\r\n\r\n- **`alpha`**: (Optional) A float representing the significance level used in the statistical tests. The default value is `0.05`, which is a common threshold for determining statistical significance. Lowering this value makes the criteria for significance stricter, while increasing it makes the criteria more lenient.\r\n\r\n- **`handle_outliers`**: (Optional) A list specifying the method and strategy for handling outliers in the `value_column`. The first element of the list is the method ('IQR' for interquartile range or 'Z-score'), and the second element is the strategy ('remove', 'impute', or 'cap'). If `None`, no outlier handling is performed.\r\n\r\n - `IQR`: Identifies outliers based on the interquartile range. Typically, values below Q1-1.5*IQR or above Q3+1.5*IQR are considered outliers.\r\n - `Z-score`: Identifies outliers based on the Z-score, with values typically beyond 3 standard deviations from the mean considered outliers.\r\n - `remove`: Outliers are removed from the dataset.\r\n - `impute`: Outliers are replaced with a specified statistic (e.g., the median).\r\n - `cap`: Outliers are capped at a specified maximum and/or minimum value.\r\n\r\n- **`mc_correction`**: (Optional) A string specifying the method for multiple comparisons correction when conducting multiple statistical tests. This argument is critical for controlling the family-wise error rate or the false discovery rate in experiments involving multiple group comparisons. Options include:\r\n\r\n - `None`: No correction is applied.\r\n - `'bonferroni'`: The Bonferroni correction, which adjusts p-values by multiplying them by the number of comparisons.\r\n - `'fdr'`: The False Discovery Rate correction, which controls the expected proportion of incorrectly rejected null hypotheses.\r\n - `'holm'`: The Holm-Bonferroni method, a step-down procedure that is less conservative than the standard Bonferroni correction.\r\n\r\n\r\n- The function returns a Pandas DataFrame (`df_results`) containing the results of the statistical tests, including test names, p-values, effect sizes, power analyses (where applicable), and detailed interpretations of the results. The DataFrame provides a comprehensive summary of the findings from the A/B test analysis.\r\n\r\n\r\n### Advanced Modeling Function Documentation\r\n\r\n#### Purpose\r\n\r\nThe `modeling` is a sophisticated function designed for conducting advanced statistical modeling. It fits different types of statistical models to explore relationships between variables, quantify effects, and provide insights into the data structure.\r\n\r\n#### Function Map\r\n\r\n<img src=\"https://github.com/knowusuboaky/ab_testing_module/blob/main/README_files/figure-markdown/mermaid-figure-2.png?raw=true\" width=\"1526\" height=\"459\" alt=\"Optional Alt Text\">\r\n\r\n#### Parameters\r\n\r\n- `data`: Pandas DataFrame containing the dataset.\r\n- `group_column`: String specifying the column with the group information.\r\n- `value_column`: String specifying the column with the dependent variable.\r\n- `control_group`: (Optional) String specifying the label of the control group.\r\n\r\n#### Functionality\r\n\r\n- Automatically determines the model type based on the dependent variable's characteristics.\r\n- Fits the chosen model and generates summaries, including parameter estimates and their significance.\r\n- Performs effect size and power analysis where applicable.\r\n- Provides detailed interpretations of the modeling results.\r\n\r\n#### Output\r\n\r\n- Model summaries for each fitted model.\r\n- Structured interpretations of each model's results.\r\n- Effect sizes and results of power analyses for the conducted models.\r\n\r\n\r\n#### Installation\r\n\r\nThis is the environment we need to load.\r\n\r\n``` bash\r\n\r\npip install ab_testing_module==3.1.7\r\n```\r\n\r\n#### Load Package\r\n\r\n``` bash\r\n\r\nfrom ab_testing_module import modeling\r\n```\r\n\r\n#### Base Operations\r\n\r\n``` bash\r\n\r\n# Perform advanced modeling\r\nmodel_summaries, interpretations, model_summaries_df, interpretations_df = modeling(data=df, \r\n group_column='group', \r\n value_column='outcome', \r\n control_group='control')\r\n```\r\n\r\n\r\nThe `modeling` function conducts advanced statistical modeling to explore relationships between variables in the context of A/B testing.\r\n\r\n\r\n- **`data`** (DataFrame): The dataset for modeling, including independent variables and the dependent variable of interest.\r\n- **`group_column`** (str): The column in `data` that identifies the grouping of data points (e.g., different conditions or treatments).\r\n- **`value_column`** (str): The column in `data` representing the dependent variable or outcome of interest.\r\n- **`control_group`** (str, optional): Specifies the label of the control group for comparative modeling purposes.\r\n\r\n\r\nThe function returns a tuple containing four elements, designed to provide both the raw statistical outputs from the modeling process and their synthesized interpretations for easier understanding:\r\n\r\n- **`model_summaries`**: A list or other collection type that includes detailed summaries for each of the statistical models fitted during the analysis. These summaries typically contain information on model coefficients, p-values, confidence intervals, and other pertinent statistical metrics.\r\n\r\n- **`interpretations`**: This output consists of narrative interpretations for each fitted model. It aims to translate the technical statistical findings into more accessible insights, highlighting significant results and their potential implications.\r\n\r\n- **`model_summaries_df`** (`Pandas DataFrame`): A DataFrame consolidating the statistical summaries of all models into a tabular format. This structure facilitates a straightforward comparison across models, offering a clear view of the estimated effects and their statistical significance.\r\n\r\n- **`interpretations_df`** (`Pandas DataFrame`): Similar to `model_summaries_df`, this DataFrame organizes the interpretations of each model's results into a structured table. It provides a narrative summary of the findings in an easily digestible format, suitable for reporting or further discussion.\r\n\r\n### Data Visualization Function Documentation\r\n\r\n#### Purpose\r\n\r\nThe `data_viz` function creates a series of data visualizations based on a provided dataset. It aims to facilitate data exploration, presentation, and the comprehension of statistical analyses through visual means.\r\n\r\n\r\n#### Function Map\r\n\r\n<img src=\"https://github.com/knowusuboaky/ab_testing_module/blob/main/README_files/figure-markdown/mermaid-figure-3.png?raw=true\" width=\"1526\" height=\"459\" alt=\"Optional Alt Text\">\r\n\r\n#### Parameters\r\n\r\n- `data`: Pandas DataFrame with the dataset to be visualized.\r\n- `group_column`: String specifying the column with group labels.\r\n- `value_column`: String specifying the column with values to analyze.\r\n- `viz_types`: List of strings indicating the types of visualizations to generate.\r\n\r\n\r\n#### Functionality\r\n\r\n- Selects and customizes visualizations based on `viz_types`.\r\n- Handles grouping and value considerations for data segmentation.\r\n- Generates data-driven interpretations for each visualization.\r\n- Employs interactive and aesthetically pleasing visualization techniques.\r\n\r\n#### Output\r\n\r\n- Displays requested visualizations.\r\n- Provides interpretations for each visualization to highlight key findings.\r\n\r\n\r\n#### Installation\r\n\r\nThis is the environment we need to load.\r\n\r\n``` bash\r\n\r\npip install ab_testing_module==3.1.7\r\n```\r\n\r\n#### Load Package\r\n\r\n``` bash\r\n\r\nfrom ab_testing_module import data_viz\r\n```\r\n\r\n#### Base Operations\r\n\r\n``` bash\r\n\r\n# Create data visualization\r\nvisualizations = data_viz(data=df, \r\n group_column='group', \r\n value_column='outcome',\r\n viz_types=['boxplot', 'violinplot', 'histogram', 'countplot', 'heatmap'])\r\n```\r\n\r\n\r\nThe `data_viz` function creates a series of visualizations to aid in the exploratory data analysis and presentation of findings from the dataset.\r\n\r\n\r\n- **`data`** (DataFrame): The dataset to be visualized, should include the variable(s) for grouping and the variable of interest.\r\n- **`group_column`** (str): The column name in `data` that contains the labels for different groups or categories within the data.\r\n- **`value_column`** (str): The column name in `data` that contains the values or outcomes to be visualized and analyzed.\r\n- **`viz_types`** (list of str): A list specifying the types of visualizations to generate, such as `['boxplot', 'violinplot', 'histogram', 'countplot', 'heatmap']`.\r\n\r\n\r\n\r\n## Ideal Use Cases\r\n\r\nThe Statistical Analysis Toolbox is designed to support a wide range of data analysis scenarios, particularly those involving A/B testing, statistical modeling, and data visualization. Below are the ideal use cases for each of the key functions within the toolbox: `ab_test`, `modeling`, and `data_viz`.\r\n\r\n### `ab_test` Function\r\n\r\n#### Ideal Use Cases\r\n\r\n- **Comparing Conversion Rates**: Ideal for analyzing the effectiveness of two different webpage designs on user conversion rates. The `ab_test` function can statistically determine if one design leads to significantly higher conversions than the other.\r\n- **Evaluating Marketing Strategies**: Useful for assessing the impact of different marketing campaigns or strategies on sales or customer engagement metrics.\r\n- **Product Feature Testing**: When introducing new features or changes to a product, `ab_test` can help evaluate the change's impact on user behavior or satisfaction.\r\n\r\n### `modeling` Function\r\n\r\n#### Ideal Use Cases\r\n\r\n- **Predictive Modeling**: For scenarios where the goal is to predict outcomes based on a set of variables, such as forecasting sales based on historical data and market conditions.\r\n- **Causal Inference**: In situations where understanding the causal relationship between variables is crucial, such as determining the effect of educational interventions on student performance.\r\n- **Complex Comparative Analysis**: Suitable for analyzing datasets with multiple groups and variables, where simple statistical tests are insufficient. For example, comparing the effects of various teaching methods across different schools or demographic groups.\r\n\r\n### `data_viz` Function\r\n\r\n#### Ideal Use Cases\r\n\r\n- **Data Exploration**: Before diving into complex analyses, `data_viz` can help uncover patterns, trends, and outliers in the data, guiding further investigation.\r\n- **Reporting and Presentation**: Creating compelling visual representations of analysis results for reports, presentations, or dashboards. It's particularly useful for conveying findings to non-technical stakeholders.\r\n- **Comparative Analysis Visualization**: When the goal is to visually compare outcomes across different groups, such as visualizing the distribution of customer satisfaction ratings before and after implementing a customer service improvement plan.\r\n\r\n\r\n## Contributing\r\n\r\nWe welcome contributions, suggestions, and feedback to make this library\r\neven better. Feel free to fork the repository, submit pull requests, or\r\nopen issues.\r\n\r\n## Documentation & Examples\r\n\r\nFor documentation and usage examples, visit the GitHub repository:\r\nhttps://github.com/knowusuboaky/ab_testing_module\r\n\r\n**Author**: Kwadwo Daddy Nyame Owusu - Boakye\\\r\n**Email**: kwadwo.owusuboakye@outlook.com\\\r\n**License**: MIT\r\n",
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