Quantitative Reasoning and Data Interpretation
Domain scope
Quantitative Reasoning and Data Interpretation tests whether you can use border data to support a decision: duty calculations, rates, yields, trends and risk indicators. The examiner rewards a clear base and a stated assumption.
Core rules
- Percentages need a base. A 10% duty on a CIF value of 20,000 is 2,000; a further 20% excise on value plus duty uses the 22,000 base, not 20,000.
- A change from 12% to 15% is 3 percentage points but a 25% relative increase. Do not mix the two.
- Averages of percentages must be weighted by the relevant denominator: 30 seizures from 200 inspections (15%) and 60 from 500 (12%) combine to 90/700 = 12.86%, not 13.5%.
- Risk yield = true positives / selected cases. Also track false positives and false negatives; a high hit rate may simply mean narrow targeting.
- Data quality checks include missing values, duplicated declarations, currency and unit errors, and changes in classification or reporting rules.
Worked example
A team inspects 200 consignments and makes 30 seizures. A new risk rule inspects 500 and makes 60 seizures. Reasoning: old yield 15%, new yield 12%, so the new rule has a lower hit rate. But the new rule inspected more than twice as many consignments and produced twice as many seizures. Before calling it worse, compare the cost per seizure, the value recovered and the false-positive burden. The correct conclusion is conditional, not 'the new rule is worse'.
Traps
- Adding percentages instead of weighting them.
- Using the wrong denominator (declarations versus consignments versus value).
- Confusing correlation with causation in seizure trends.
- Ignoring currency conversion and rounding.
- Reporting a single yield figure without the inspection volume.