Data Interpretation
Data interpretation is CLAT's signature quant format — every question here is really a percentage, ratio, or average question wrapped inside a table or chart, testing careful reading as much as calculation.
10 questions · 5 minutes · instant scoring
What this topic actually tests
CLAT presents most of its quantitative section as short passages of data — a table, a set of survey figures, or a simple chart — followed by two to four questions that reuse the same figures. The core skill is not new mathematics but disciplined reading: before calculating anything, identify exactly what each row, column, or category represents, and note any totals or relationships stated in the passage text itself, since these are often needed for later questions even if the first question does not use them. The fastest approach is to compute and jot down key totals (row sums, column sums, grand totals) once, immediately after reading the passage, rather than re-deriving them for each sub-question — this front-loading saves time across a 3-4 question set built on one data table. For percentage-growth-between-two-periods questions, always compute (new value - old value)/old value x 100 using the earlier period as the base, exactly as in standalone percentage questions — DI does not change the underlying formula, only the source of the numbers. For pie-chart passages, remember that the percentages given usually sum to 100% and represent parts of one clearly stated total; convert each percentage to an actual value using that total before comparing categories, since percentages of different totals cannot be compared directly. Worked example: a survey of 500 aspirants finds 240 preferred Legal Reasoning first and 150 preferred Logical Reasoning first; the number preferring some other section first is found by simple subtraction, 500 - 240 - 150 = 110, and this residual-by-subtraction technique is one of the most frequently tested DI skills, since passages rarely state every category explicitly.
The common trap on this topic
The most common DI trap is reading a value off the wrong row or column of a table under time pressure — because DI passages pack several numbers into a small space, a single misread figure propagates an error through every subsequent question in that set, unlike standalone questions where an error is isolated. A second frequent trap is comparing percentages across two different totals as if they were comparable raw numbers — for instance, assuming a category that is '40% of Group A' is larger than one that is '35% of Group B' without checking whether Group A and Group B are the same size; percentages must be converted back to actual values before any such comparison is valid. A third trap specific to 'growth from period 1 to period 2' DI questions is using the wrong base year — some passages present multiple time periods, and it is easy to accidentally compute growth relative to the most recent period shown rather than the truly original (earliest) period the question is asking about; always re-read exactly which two periods the specific sub-question names, since a table with three or more columns of data may support several different pairwise comparisons that use different bases.
Take the micro-test
Which batch showed the highest percentage growth in enrollment from 2023 to 2024?
What is the total enrollment across all five batches in 2024?
By how much did Batch C's enrollment decrease from 2023 to 2024?
What was the overall percentage growth in total enrollment (all batches combined) from 2023 to 2024, to the nearest whole percent?
How many aspirants preferred starting with a section other than Legal Reasoning or Logical Reasoning?
How many aspirants who preferred Legal Reasoning first scored above 80 marks in that section?
What percentage of the total 500 aspirants preferred starting with Legal Reasoning?
How many students are in the Science stream?
What is the ratio of students in the Commerce stream to students in the Law stream?
How many Law stream students are preparing specifically for CLAT?
FAQ
How should I approach a data interpretation passage with multiple questions attached?
Read the entire passage and data set first, and calculate obvious totals (row sums, column sums, grand total) before looking at the individual questions — this avoids recalculating the same totals repeatedly and reduces careless misreads under time pressure.
Is DI harder mathematically than standalone quant questions?
No — the underlying calculations (percentages, ratios, averages) are usually simpler than standalone questions on those topics. The added difficulty is entirely in accurately locating and interpreting the correct figures from a table or chart, not in the arithmetic itself.
How do I handle a DI question that asks for a value not directly stated in the table?
Look for it as a residual — total minus all stated categories — which is one of the most common DI question types; CLAT tables rarely state every single category explicitly, expecting you to derive the missing one by subtraction.
Keep practising
Ready to see the full picture?
This micro-test covers one topic. The full CLAT diagnostic covers all five sections in 30 minutes and gives you a section-wise breakdown of where you actually stand.
Take the Full Diagnostic →