DI โ Caselet Data Interpretation: Summary
Caselet DI presents the data as a paragraph of text, not a chart or table โ you must extract and organise the numbers yourself before solving. This is CAT's signature DI-LR hybrid: the hard part is modelling the data, not the arithmetic. High weight in recent DILR sets.
Why caselets are hard
The information is scattered across sentences, often with conditions ("only if", "twice as many", "the rest"). Success = converting prose into a clean table, Venn diagram, or equation set, then reading answers off your own structure.
The extraction toolkit
| Prose cue | Model it as |
|---|---|
| "twice as many", "30% more" | An equation / ratio |
| "the rest", "remaining" | Total minus the known parts |
| "both", "neither", "only" | A Venn diagram / set logic |
| Sequential conditions | A table you fill row by row |
Method
- Read the full caselet once; identify the entities and the quantity being tracked.
- Build the right container โ table, Venn, or equations โ and translate each sentence into it.
- Fill knowns, then derive unknowns using totals and given relations.
- Answer questions from your completed structure, not by re-reading prose.
Exam Tricks & Tips
- ๐ฏ Draw the container first. A table/Venn you build once answers all 3โ4 questions; re-reading prose per question wastes minutes.
- ๐ฏ "The rest" = total โ sum of knowns โ a caselet's favourite hidden step.
- ๐ฏ Translate comparatives into equations immediately ("A had 20% more than B" โ A = 1.2B).
- ๐ฏ For overlaps, use the set formula: n(AโชB) = n(A) + n(B) โ n(AโฉB); extend to three sets when "all three" appears.
- ๐ฏ Assign variables to the true unknowns only, then use totals to pin them โ don't over-variable.
- โ Common mistake: starting arithmetic before fully modelling the data โ half-read caselets produce confident wrong answers.
Expected exam pattern
1โ2 caselet sets in DILR, 3โ4 questions each, often the hardest sets in the section. Themes: sales/inventory, survey overlaps, tournaments, resource allocation. MCQ + TITA. Reward: whoever models cleanly; the arithmetic itself is usually light.
Quick recap
Caselets hide a table/Venn/equation inside prose โ extract it first, fill knowns, derive the rest with totals and relations. "The rest" means total minus knowns; overlaps use the set-union formula. Model fully before you calculate.
DI โ Caselet Data Interpretation: Flashcards
Cover the answer, recall, then check. 12 cards on caselet DI.
Q1. How is caselet data presented?
A1. As a paragraph of text โ you must extract and organise the numbers yourself.
Q2. What is the hard part of a caselet?
A2. Modelling the data into a table/Venn/equation set โ the arithmetic is usually light.
Q3. How do you model "twice as many" or "30% more"?
A3. As an equation/ratio (e.g. A = 1.3B).
Q4. What does "the rest / remaining" mean?
A4. Total minus the sum of the known parts.
Q5. When do you reach for a Venn diagram?
A5. When the prose has "both", "neither", "only", or overlapping categories.
Q6. State the two-set union formula.
A6. n(AโชB) = n(A) + n(B) โ n(AโฉB).
Q7. Why draw the container before answering?
A7. One table/Venn answers all questions in the set; re-reading prose each time wastes time.
Q8. First step in reading a caselet?
A8. Identify the entities and the quantity being tracked.
Q9. How many variables should you assign?
A9. Only for true unknowns; use totals/relations to pin them, don't over-variable.
Q10. The classic caselet mistake?
A10. Doing arithmetic before fully modelling the data.
Q11. How hard are caselets relative to other DI?
A11. Often the hardest sets in DILR โ modelling-heavy.
Q12. Typical caselet themes?
A12. Sales/inventory, survey overlaps, tournaments, resource allocation.
DI โ Caselet Data Interpretation
Caselets replace charts with paragraphs โ the data is buried in prose, and often incomplete, requiring you to build the table yourself before any question can be answered. This is the most CAT-like DI form because it fuses reading comprehension, logical deduction, and calculation. The winners are those who convert the text into a structured grid and fill gaps by inference before attempting a single question.
What this tests
Data structuring and deduction โ extracting scattered numerical facts from prose, organising them into a table, and inferring missing values from constraints.
The method
Beginner โ extract every fact into a grid
Read the caselet once and draw the table it describes: rows and columns for the entities and attributes mentioned. Enter every explicit number. Mark what's unknown with blanks. Do not attempt questions until the grid exists โ the prose is deliberately non-linear and you'll re-read endlessly otherwise.
Intermediate โ fill blanks by constraint
Caselets rarely give all values directly; they give relationships: "P is twice Q", "the total is 500", "R exceeds S by 20". These are equations. Use given totals (row/column sums), ratios, and differences to solve for blanks โ exactly like a mini system of equations. Often one deduced value unlocks a chain of others.
Advanced โ the FAST CAT approach: prioritise, and answer partial questions early
- Order the questions: some ask only about values you already have โ answer those first for quick marks, and let harder ones wait until more of the grid is filled.
- Look for a key constraint that cracks the set โ a single total or ratio that, once applied, determines several unknowns. Caselet sets usually have one such "keystone".
- Track degrees of freedom: if the data determines a unique solution, keep deducing; if it leaves options open, some questions may be "cannot be determined".
- Keep your grid neat and updated โ a messy scratch table causes arithmetic errors that snowball across all questions.
Worked example
Caselet: "A shop sold shirts, trousers, and caps. Total items sold = 100. Shirts sold were twice the trousers. Caps sold were 10 fewer than trousers."
Question: How many trousers were sold?
Let trousers = T. Shirts = 2T. Caps = T โ 10. Sum: 2T + T + (T โ 10) = 100 โ 4T โ 10 = 100 โ 4T = 110 โ T = 27.5. That's not an integer, signalling we should re-read โ suppose "caps were 10 fewer than shirts": Caps = 2T โ 10. Then 2T + T + (2T โ 10) = 100 โ 5T = 110 โ T = 22. Shirts = 44, caps = 34, trousers = 22, sum = 100. โ Answer: 22 trousers. The lesson: translate each sentence into an equation, use the total as the closing constraint, and let a non-integer result flag a mis-reading to correct.
CAT relevance
Caselets are a signature CAT DILR format โ often the hardest, most differentiating sets, and increasingly the majority of the section. They demand the RC skill of careful reading fused with algebraic deduction. Building the grid reliably is the single highest-leverage DILR habit.
Speed tricks & shortcuts
- Build the table before answering anything โ externalise the prose into structure.
- Translate relational sentences ("twice", "10 fewer", "total") into equations.
- Find the keystone constraint (a total/ratio) that unlocks multiple unknowns.
- Answer already-solvable questions first; defer the ones needing more deduction.
- Mnemonic โ "Prose to grid, then decide."
Attempting questions straight from the paragraph without building the grid, and re-reading the text for each question. The data is scattered on purpose; without a structured table you waste time and miss the relationships that fill the blanks.
- โ- Convert the prose into a table before touching questions.
- โ- Fill blanks using totals, ratios, and differences as equations.
- โ- Find the keystone constraint that cracks several unknowns at once.
- โ- Sequence questions โ solve the already-answerable ones first.
- โ- A non-integer/impossible result flags a mis-read to fix.
- โCaselet DI hides a table inside a story. Extract every fact into a grid, turn relationships into equations, solve the blanks with totals and ratios, then let the questions fall โ structure first, answers second.
DI โ Caselet Data Interpretation โ Formula Sheet
Key formulas
- Translate the paragraph into a table of values first.
- Percentage = part/whole ร100; ratio and average as usual.
- Set up equations for unknowns implied by the text.
- Track totals and sub-totals carefully.
- Cross-check derived values against stated totals.
- โ- Convert the paragraph into a table.
- โ- Percentage = part/whole ร100.
- โ- Form equations for hidden unknowns.
- โ- Verify against stated totals.
Usage: tabulate the caselet before attempting any question.
DI โ Caselet Data Interpretation โ Worked Example
Worked Example
Problem: (Caselet โ data given only in words; you must extract it.) A shop sold 500 items in a week. 60% were electronics; the rest were accessories. Of the electronics, one-fourth were returned; of the accessories, 10% were returned. Find (a) how many items were returned in total, and (b) returned items as a percentage of all items sold.
Solution:
A caselet gives no chart or table โ the data is embedded in prose, so the first job is to organise it into numbers, ideally a small table.
Extract and structure:
- Total items sold = 500.
- Electronics = 60% of 500 = 0.60 ร 500 = 300.
- Accessories = the rest = 500 โ 300 = 200 (i.e. 40%).
| Category | Sold | Return rate | Returned |
|---|---|---|---|
| Electronics | 300 | 1/4 = 25% | 75 |
| Accessories | 200 | 10% | 20 |
Compute returns:
- Electronics returned = one-fourth of 300 = 300/4 = 75.
- Accessories returned = 10% of 200 = 0.10 ร 200 = 20.
(a) Total returned = 75 + 20 = 95 items.
(b) Returned as a percentage of all sold = 95 / 500 ร 100 = 9500/500 = 19%.
The discipline for caselets: convert every verbal fact into a value in a table BEFORE answering; here that means resolving "the rest" (200), "one-fourth" (75), and "10%" (20) into concrete counts, which then combine cleanly. Skipping the table is where caselet errors creep in.
Answer: (a) 95 items returned (75 electronics + 20 accessories). (b) 95/500 = 19% of all items sold were returned.
- โ- Caselets hide the data in prose โ first extract it into a structured table before calculating.
- โ- Resolve relative phrases ("the rest", "one-fourth", "10% of") into absolute counts step by step.
- โ- Answer only after the table is complete; combining the concrete counts is then straightforward.