section 6 in report has explainer text when p > 0.05

Author: devhelpr

src/assets/bias-detection-python-code.tsx

@@ -614,7 +614,15 @@ def run():
                 'titleKey': "biasAnalysis.distributionOfFeaturesAcrossClustersAccordeonTitle",
                 'defaultValue': columns_to_analyze[0]                
             }))
-
+    else:
+        setResult(json.dumps({
+            'type': 'text',
+            'key': 'biasAnalysis.distribution.noResultsClusterDifferences',
+            'params': {
+                'biasVariable': bias_variable
+            }
+        }))
+            
     df_most_biased_cluster = most_biased_cluster_df
     df_other = rest_df
     

src/locales/en.ts

@@ -393,6 +393,7 @@ For categorical data a two-sided Z-test, while for numerical data a two-sided t-
         distribution: {
             mainHeading: '6. Cluster differences wrt. features',
             heading: '"{{variable}}" distribution per cluster:',
+            noResultsClusterDifferences: `No results for cluster differences with respect to features are shown, because there is no statistically significant difference in {{biasVariable}} between the most deviating cluster and the rest of the dataset.`,
         },
         splittingDataset: {
             heading: '3. Splitting dataset',

src/locales/nl.ts

@@ -350,6 +350,7 @@ Een eenzijdige Z-toets wordt uitgevoerd:
         distribution: {
             mainHeading: '6. Cluster verschillen mbt. eigenschappen',
             heading: '"{{variable}}" verdeling per cluster:',
+            noResultsClusterDifferences: `Er worden geen resultaten getoond voor clusterverschillen met betrekking tot kenmerken, omdat er geen statistisch significant verschil is in {{biasVariable}} tussen het meest afwijkende cluster en de rest van de dataset.`,
         },
         splittingDataset: {
             heading: '3. Splitsen dataset',