Implement error handling for evaluate_contingency_table()

Implementation Summary

evaluate_contingency_table() is equipped with robust error handling mechanisms to ensure the validity and integrity of input data and parameters before proceeding with statistical test evaluations. This approach enhances the function's reliability and usability by preventing common errors and ensuring appropriate feedback is provided to the user.

Detailed Error Handling Breakdown

Input Type Validation

DataFrame Type Check
- Ensures the contingency_table is a pandas DataFrame, which is essential since the function's operations are designed for DataFrame structures.

if not isinstance(contingency_table, pd.DataFrame):
    raise TypeError("evaluate_contingency_table(): The 'contingency_table' parameter must be a pandas DataFrame.")

Integer Type Check for min_sample_size_yates
- Validates that the min_sample_size_yates parameter is an integer, as it is used in numerical comparisons to determine test viability.

if not isinstance(min_sample_size_yates, int):
    raise TypeError("evaluate_contingency_table(): The 'min_sample_size_yates' parameter must be an integer.")

Boolean Type Check for pipeline and quiet
- Confirms that both pipeline and quiet are boolean values, influencing the output format and verbosity of the function respectively.

if not isinstance(pipeline, bool):
    raise TypeError("evaluate_contingency_table(): The 'pipeline' parameter must be a boolean.")
if not isinstance(quiet, bool):
    raise TypeError("evaluate_contingency_table(): The 'quiet' parameter must be a boolean.")

Input Value Validation

DataFrame Emptiness Check
- Checks that the contingency_table is not empty, which is crucial for performing any statistical evaluations.

if contingency_table.empty:
    raise ValueError("evaluate_contingency_table(): The 'contingency_table' parameter must not be empty.")

Positive Integer Check for min_sample_size_yates
- Ensures that min_sample_size_yates is a positive integer, validating the logic for applying Yates' correction based on sample size criteria.

if min_sample_size_yates <= 0:
    raise ValueError("evaluate_contingency_table(): The 'min_sample_size_yates' parameter must be a positive integer.")

Conclusion

The error handling implemented in evaluate_contingency_table() meticulously checks for common input errors, ensuring that only appropriate and correctly formatted data is processed. This thorough validation process not only prevents runtime errors but also guides users in correcting input mistakes, thereby facilitating a more effective and user-friendly statistical analysis experience.

ETA444 / datasafari