update

util/analysis.py

@@ -304,22 +304,27 @@ def result_evaluation(test_results, test_type='multiple'):
     # Statistical tests evaluation
     for test_name, result in test_results.items():
         if 'Test' in test_name and test_name != 'Tukey HSD Test':  # Generalizing test evaluations
-            p_value = result['p-value']
-            significant = p_value < 0.05
-            test_label = test_name.replace('_', ' ').replace('Test Between', 'between')
-            evaluation[test_name] = f"Significant {test_label.lower()} observed (p = {p_value:.5f}), indicating potential biases." if significant else f"No significant {test_label.lower()}."
+            if isinstance(result, dict) and 'p-value' in result:
+                p_value = result['p-value']
+                significant = p_value < 0.05
+                test_label = test_name.replace('_', ' ').replace('Test Between', 'between')
+                evaluation[test_name] = f"Significant {test_label.lower()} observed (p = {p_value:.5f}), indicating potential biases." if significant else f"No significant {test_label.lower()}."
+            else:
+                evaluation[test_name] = "Test result format error or incomplete data."
 
     # Special case evaluations
     if 'Wilcoxon Test Between Pairs' in test_results:
-        if isinstance(test_results['Wilcoxon Test Between Pairs'], str):  # Handle small sample size message
-            evaluation['Wilcoxon Test Between Pairs'] = test_results['Wilcoxon Test Between Pairs']
+        wilcoxon_result = test_results['Wilcoxon Test Between Pairs']
+        if isinstance(wilcoxon_result, float):
+            evaluation['Wilcoxon Test Between Pairs'] = f"Significant rank difference between {variables[0]} and {variables[1]} (p = {wilcoxon_result:.5f}), indicating bias." if wilcoxon_result < 0.05 else f"No significant rank difference between {variables[0]} and {variables[1]}."
         else:
-            p_value = test_results['Wilcoxon Test Between Pairs']
-            evaluation['Wilcoxon Test Between Pairs'] = f"Significant rank difference between {variables[0]} and {variables[1]} (p = {p_value:.5f}), indicating bias." if p_value < 0.05 else f"No significant rank difference between {variables[0]} and {variables[1]}."
+            evaluation['Wilcoxon Test Between Pairs'] = wilcoxon_result  # Presuming it's an error message or non-numeric value
 
     # ANOVA and Tukey HSD tests
     if test_type == 'multiple':
-        evaluation['ANOVA Test'] = test_results['ANOVA Test']
-        evaluation['Tukey HSD Test'] = test_results['Tukey HSD Test']
+        anova_p = test_results['ANOVA Test'].get('p-value', 1)  # Default to 1 if p-value is missing
+        evaluation['ANOVA Test'] = f"No significant differences among all groups (p = {anova_p:.5f}), no further post-hoc analysis required." if anova_p >= 0.05 else test_results['ANOVA Test']
+        evaluation['Tukey HSD Test'] = test_results.get('Tukey HSD Test', 'Tukey test not performed or data missing.')
 
     return evaluation
+