Update TooltipWrapper styling for improved visual clarity; enhance localization for missing data descriptions with clearer tooltip references and structured explanations.

Author: devhelpr

src/components/TooltipWrapper.tsx

@@ -25,7 +25,7 @@ export function TooltipWrapper({
             <TooltipProvider>
                 <Tooltip>
                     <TooltipTrigger asChild>
-                        <span className="border-b border-dotted border-gray-400 cursor-help">
+                        <span className="border-b-2 border-dashed border-gray-600 cursor-help">
                             {children}
                         </span>
                     </TooltipTrigger>

src/locales/en.ts

@@ -159,27 +159,23 @@ export const en = {
             'The figures below display the differences in value frequency for a combination of variables. For comparing two categorical variables, bar charts are plotted. For comparing a numerical and a categorical variables, a so called [violin plot](https://en.wikipedia.org/wiki/Violin_plot) is shown. For comparing two numercial variables, a [LOESS plot](https://en.wikipedia.org/wiki/Local_regression) is created. For all plots holds: the synthetic data is of high quality when the shape of the distributions in the synthetic data equal the distributions in the real data.',
         moreInfo:
             'Do you want to learn more about synthetic data?\n  \n  \n  \n- [python-synthpop on Github](https://github.com/NGO-Algorithm-Audit/python-synthpop)\n- [local-first web app on Github](https://github.com/NGO-Algorithm-Audit/local-first-web-tool/tree/main)\n- [Synthetic Data: what, why and how?](https://royalsociety.org/-/media/policy/projects/privacy-enhancing-technologies/Synthetic_Data_Survey-24.pdf)\n- [Knowledge Network Synthetic Data](https://online.rijksinnovatiecommunity.nl/groups/399-kennisnetwerk-synthetischedata/welcome) (for Dutch public organizations)\n- [Synthetic data portal of Dutch Executive Agency for Education](https://duo.nl/open_onderwijsdata/footer/synthetische-data.jsp) (DUO)\n- [CART: synthpop resources](https://synthpop.org.uk/resources.html)\n- [Gaussian Copula - Synthetic Data Vault](https://docs.sdv.dev/sdv)',
-        missingData: `For {tooltip:syntheticData.missingDataTooltip}Missing At Random (MAR){/tooltip} and {tooltip:syntheticData.missingDataTooltip}Missing Not At Random (MNAR){/tooltip} data, 
-we recommend to impute the missing data. For Missing Completely At Random (MCAR), we recommend to remove the missing data. {tooltip:syntheticData.missingDataTooltip}See the info box for more information{/tooltip}.`,
-        missingDataTooltip: `MCAR, MAR, and MNAR are terms used to describe different mechanisms of missing data:
-
-1. **MCAR (Missing Completely At Random)**:
-- The probability of data being missing is completely independent of both observed and unobserved data. 
-- There is no systematic pattern to the missingness.
-- Example: A survey respondent accidentally skips a question due to a printing error.
-- Recommendation: remove missing data.
-
-2. **MAR (Missing At Random)**:
+        missingData: `For {tooltip:syntheticData.missingDataMARTooltip}Missing At Random (MAR){/tooltip} and {tooltip:syntheticData.missingDataMNARTooltip}Missing Not At Random (MNAR){/tooltip} data, 
+we recommend to impute the missing data. For {tooltip:syntheticData.missingDataMCARTooltip}Missing Completely At Random (MCAR){/tooltip}, we recommend to remove the missing data.`,
+        missingDataMARTooltip: `**MAR (Missing At Random)**:
 - The probability of data being missing is related to the observed data but not the missing data itself.
 - The missingness can be predicted by other variables in the dataset.
 - Example: Students' test scores are missing, but the missingness is related to their attendance records.
-- Recommendation: impute missing data.
-
-3. **MNAR (Missing Not At Random)**:
+- Recommendation: impute missing data.`,
+        missingDataMNARTooltip: `**MNAR (Missing Not At Random)**:
 - The probability of data being missing is related to the missing data itself. 
 - There is a systematic pattern to the missingness that is related to the unobserved data.
 - Example: Patients with more severe symptoms are less likely to report their symptoms, leading to missing data that is related to the severity of the symptoms.
 - Recommendation: impute missing data.`,
+        missingDataMCARTooltip: `**MCAR (Missing Completely At Random)**:
+- The probability of data being missing is completely independent of both observed and unobserved data. 
+- There is no systematic pattern to the missingness.
+- Example: A survey respondent accidentally skips a question due to a printing error.
+- Recommendation: remove missing data.`,
     },
 
     biasAnalysis: {