Implement error handling for hypothesis_predictor_core_c()

[ETA444]

Implementation Summary

The function hypothesis_predictor_core_c() performs statistical tests to evaluate the association between two categorical variables using a contingency table. It takes into account the viability of various statistical tests to decide which tests are appropriate given the characteristics of the data. This error handling ensures the inputs are correctly formatted and appropriate for the analysis to prevent runtime errors and logical inconsistencies.

Detailed Error Handling Breakdown

Type Validations

• Contingency Table Type Check
  • Ensures that the contingency_table is a pandas DataFrame. This is essential because the function relies on DataFrame methods to compute statistical tests.

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

• Boolean Type Checks for Test Viability Flags
  • Checks that chi2_viability, barnard_viability, boschloo_viability, fisher_viability, and yates_correction_viability are all booleans. This is crucial because these flags determine which statistical tests are appropriate based on pre-evaluated criteria.

if not isinstance(chi2_viability, bool):
    raise TypeError("hypothesis_predictor_core_c(): The 'chi2_viability' parameter must be a boolean.")
# Similar checks for barnard_viability, boschloo_viability, fisher_viability, and yates_correction_viability

• String Type Check for alternative
  • Verifies that the alternative parameter is a string, which is necessary for specifying the hypothesis direction in exact tests.

if not isinstance(alternative, str):
    raise TypeError("hypothesis_predictor_core_c(): The 'alternative' parameter must be a string.")

Value Validations

• Contingency Table Emptiness Check
  • Checks that the contingency table is not empty to ensure there is data available for hypothesis testing.

if contingency_table.empty:
    raise ValueError("hypothesis_predictor_core_c(): The input contingency table is empty.")

• Validation of alternative Parameter
  • Ensures the alternative parameter matches one of the expected values ('two-sided', 'less', 'greater'). This is crucial for correct interpretation and execution of exact tests.

valid_alternatives = ['two-sided', 'less', 'greater']
if alternative not in valid_alternatives:
    raise ValueError(f"hypothesis_predictor_core_c(): Invalid 'alternative' value. Expected one of {valid_alternatives}, got '{alternative}'.")

Link to Full Code