Difference between groupsStrong evidence
Monetary relief rate varies by product: Prepaid card 36.9% versus Credit reporting 0.1%
- Prepaid card37%(103)
- Credit card18%(2,296)
- Bank account15%(3,050)
- Personal loan6.3%(111)
- Money transfer6.3%(591)
- Payday loan, title loan, or personal lo…6.1%(212)
- Vehicle loan or lease3.9%(510)
- Mortgage1.9%(1,087)
- Student loan0.8%(486)
- Debt collection0.6%(2,219)
- Credit reporting0.1%(17,507)
- Credit reporting or other personal cons…0.1%(10,754)
Overall: 2.7%
Prepaid card is about 645.9 times Credit reporting.
Show the working
What this is based on
- Based on 103 Prepaid card rows and 17,507 Credit reporting rows.
- Random sampling alone would almost never produce a gap this large.
How sure we are: Strong evidence
Far larger than random ups and downs can explain.
The exact reasoning
Monetary relief rate differs by product: Prepaid card is highest at 36.9%, Credit reporting is lowest at 0.1% (64489% apart).
- Based on 103 and 17,507 rows; the gap is about 71.5 standard errors, well beyond chance.
The numbers
| Prepaid card | 36.9% | 103 records |
|---|---|---|
| Credit card | 18.3% | 2,296 records |
| Bank account | 15.5% | 3,050 records |
| Personal loan | 6.3% | 111 records |
| Money transfer | 6.3% | 591 records |
| Payday loan, title loan, or personal loan | 6.1% | 212 records |
| Vehicle loan or lease | 3.9% | 510 records |
| Mortgage | 1.9% | 1,087 records |
| Student loan | 0.8% | 486 records |
| Debt collection | 0.6% | 2,219 records |
| Credit reporting | 0.1% | 17,507 records |
| Credit reporting or other personal consumer reports | 0.1% | 10,754 records |
| Debt or credit management | 0.0% | 17 records |
Where the data came from
Your table “consumer_complaints_2023”. Nothing was changed; the analysis only read it.
The query that was run (read-only)
SELECT "product" AS grp, COUNT((CASE WHEN LOWER(TRIM("monetary_relief")) IN ('1', '1.0', 'true', 'yes', 'y', 't') THEN 1 WHEN "monetary_relief" IS NULL THEN NULL ELSE 0 END)) AS n, AVG((CASE WHEN LOWER(TRIM("monetary_relief")) IN ('1', '1.0', 'true', 'yes', 'y', 't') THEN 1 WHEN "monetary_relief" IS NULL THEN NULL ELSE 0 END)) AS mean, SUM((CASE WHEN LOWER(TRIM("monetary_relief")) IN ('1', '1.0', 'true', 'yes', 'y', 't') THEN 1 WHEN "monetary_relief" IS NULL THEN NULL ELSE 0 END)*(CASE WHEN LOWER(TRIM("monetary_relief")) IN ('1', '1.0', 'true', 'yes', 'y', 't') THEN 1 WHEN "monetary_relief" IS NULL THEN NULL ELSE 0 END)) AS sq, COUNT(CASE WHEN (CASE WHEN LOWER(TRIM("monetary_relief")) IN ('1', '1.0', 'true', 'yes', 'y', 't') THEN 1 WHEN "monetary_relief" IS NULL THEN NULL ELSE 0 END) > 0 THEN 1 END) AS npos, AVG(CASE WHEN (CASE WHEN LOWER(TRIM("monetary_relief")) IN ('1', '1.0', 'true', 'yes', 'y', 't') THEN 1 WHEN "monetary_relief" IS NULL THEN NULL ELSE 0 END) > 0 THEN ln((CASE WHEN LOWER(TRIM("monetary_relief")) IN ('1', '1.0', 'true', 'yes', 'y', 't') THEN 1 WHEN "monetary_relief" IS NULL THEN NULL ELSE 0 END)) END) AS lmean, SUM(CASE WHEN (CASE WHEN LOWER(TRIM("monetary_relief")) IN ('1', '1.0', 'true', 'yes', 'y', 't') THEN 1 WHEN "monetary_relief" IS NULL THEN NULL ELSE 0 END) > 0 THEN ln((CASE WHEN LOWER(TRIM("monetary_relief")) IN ('1', '1.0', 'true', 'yes', 'y', 't') THEN 1 WHEN "monetary_relief" IS NULL THEN NULL ELSE 0 END))*ln((CASE WHEN LOWER(TRIM("monetary_relief")) IN ('1', '1.0', 'true', 'yes', 'y', 't') THEN 1 WHEN "monetary_relief" IS NULL THEN NULL ELSE 0 END)) END) AS lsq FROM "consumer_complaints_2023" WHERE (CASE WHEN LOWER(TRIM("monetary_relief")) IN ('1', '1.0', 'true', 'yes', 'y', 't') THEN 1 WHEN "monetary_relief" IS NULL THEN NULL ELSE 0 END) IS NOT NULL AND "product" IS NOT NULL GROUP BY grp HAVING COUNT((CASE WHEN LOWER(TRIM("monetary_relief")) IN ('1', '1.0', 'true', 'yes', 'y', 't') THEN 1 WHEN "monetary_relief" IS NULL THEN NULL ELSE 0 END)) >= 10