CAT Data Interpretation (Di) Flashcards

24 sample cards from the Data Interpretation (Di) deck

Sampled from the end of the deck, so these are different cards from the ones shown on the syllabus page.

1. What is a 'novel/non-standard' format DI set and the right approach?

   An unfamiliar visual (e.g., radar/spider chart, bubble chart, tree map, scatter plot). The approach is to spend time first decoding the legend and axes, then read carefully rather than assume conventions.

2. What does a bubble chart encode beyond the x and y position?

   A third variable represented by the size (area) of each bubble; sometimes a fourth via color.

3. What does a radar (spider) chart show?

   Multivariate data on several axes radiating from a center; each axis is a variable and the plotted polygon shows one entity's profile across all variables.

4. Calculation acceleration: how to quickly find 10%, 1%, and 5% of a number?

   10% = move decimal one place left; 1% = move two places left; 5% = half of 10%. Combine these to build other percentages (e.g., 15% = 10% + 5%).

5. What is the fraction-to-percentage memorization technique and give 1/7 and 1/8.

   Memorize common reciprocals: 1/7 ≈ 14.28%, 1/8 = 12.5%. Knowing these lets you swap tedious division for quick recognition.

6. Percentage values of 1/9, 1/11, and 1/12 (memorized for DI).

   1/9 ≈ 11.11%, 1/11 ≈ 9.09%, 1/12 ≈ 8.33%.

7. Calculation acceleration: how to compare two fractions a/b and c/d quickly?

   Cross-multiply: a/b > c/d if a*d > b*c (for positive denominators). Avoids computing both decimals.

8. What is approximation/rounding strategy in DI and when is it safe?

   Round numbers to convenient values to speed computation; it is safe when answer options are widely spaced. Track the direction of rounding so the estimate stays on the correct side.

9. Calculation acceleration: estimating a percentage like 37 out of 480 quickly.

   Use anchors: 10% of 480 = 48, so 37 is below 10%; 37/480 ≈ 7.7%. Scale from easy 10%/5%/1% benchmarks rather than long division.

10. What is the 'ratio comparison via percentage change' shortcut for growth?

    For a/b vs c/d as growth rates, compare the percentage increase of numerator against denominator; the fraction rises if the numerator grows by a larger percentage than the denominator.

11. How does the digit/leading-figure approximation help in choosing among answer options?

    Compute only the leading one or two significant digits of the result; this is usually enough to match a unique answer option, saving full precision work.

12. Comparison method: how to rank several quantities given as fractions with similar values?

    Bring to a common benchmark (common numerator or common denominator) or convert to decimals only to the precision needed to break ties.

13. What is the percentage-point vs percentage-change distinction?

    A percentage point is the arithmetic difference between two percentages (e.g., 20% to 25% = 5 percentage points), while percentage change is the relative change ((25-20)/20 = 25%).

14. Comparison method: when asked 'which year had the highest growth rate' vs 'highest absolute growth', what differs?

    Growth rate uses percentage change relative to the prior value; absolute growth uses the raw difference. The same data can give different winners for each.

15. Formula: Compound Annual Growth Rate (CAGR) over n years from value V0 to Vn.

    CAGR = (Vn / V0)^(1/n) - 1, expressed as a percentage.

16. Formula: simple average growth rate over n periods.

    Average of the period-by-period percentage changes (sum of the per-period growth percentages divided by the number of periods).

17. What is Data Sufficiency in DI?

    A question format that asks not for the answer itself but whether the given statements/data are enough to answer the question uniquely.

18. State the standard five options used in two-statement Data Sufficiency questions.

    (A) Statement 1 alone sufficient, 2 not; (B) Statement 2 alone sufficient, 1 not; (C) both together sufficient but neither alone; (D) each alone sufficient; (E) both together still insufficient.

19. In Data Sufficiency, why must you avoid actually solving for the value?

    Because you only need to determine whether a unique answer is obtainable; fully solving wastes time and can bias you toward assuming sufficiency.

20. Data Sufficiency pitfall: a statement gives a quadratic yielding two valid values. Is it sufficient?

    No, not sufficient on its own, because it does not pin down a single unique value (unless context eliminates one root).

21. Data Sufficiency technique: how should you evaluate the two statements?

    Assess each statement independently first (never carry information from one into the other), then, only if needed, combine them.

22. What common assumption error should you avoid in DI when a chart omits a year or category?

    Do not assume continuity or zero for missing data; only use values explicitly given, and treat absent data as unknown rather than as zero.

23. When two bar graphs use different vertical scales, what is the comparison risk?

    Visual heights can mislead; equal bar heights may represent different magnitudes, so always read the numeric axis values rather than comparing heights across charts.

24. General DI strategy: in what order should you handle a multi-question set to maximize accuracy and speed?

    First understand the data structure and units, then attempt direct/single-step questions, defer calculation-heavy or 'cannot be determined' questions, and use approximation guided by answer-option spacing.

What this deck covers

Data Interpretation (Di) flashcards FAQ