MH CET MBA Data Interpretation & Data Sufficiency Flashcards

24 sample cards from the Data Interpretation & Data Sufficiency deck

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

1. In Data Sufficiency, what does 'sufficient' mean for a value-type (what is x?) question?

   A statement is sufficient only if it yields exactly one unique value for the quantity asked. If it allows two or more possible values, it is NOT sufficient.

2. In Data Sufficiency, what does 'sufficient' mean for a yes/no question?

   A statement is sufficient if it gives a definite, consistent answer—either always 'yes' or always 'no'. A statement that sometimes gives 'yes' and sometimes 'no' is NOT sufficient (note: a definite 'no' is still sufficient).

3. What is the correct order for evaluating the two statements in a Data Sufficiency problem?

   Evaluate Statement 1 alone first, then Statement 2 alone independently (forgetting Statement 1), and only if neither alone is sufficient, combine both together.

4. In Data Sufficiency, why must you avoid carrying information from Statement 1 when testing Statement 2 alone?

   Because each statement must be judged on its own; carrying over Statement 1's information contaminates the test of Statement 2 alone and leads to wrongly choosing (A)/(B)/(D).

5. In quant-based Data Sufficiency involving equations, what is the rule of thumb relating equations to unknowns?

   To solve for n unknowns you generally need n independent linear equations. A statement giving fewer independent equations than unknowns is usually insufficient—but watch for special constraints (e.g., integers, positives) that can pin a unique solution.

6. Why is the equation-counting rule only a guideline, not a law, in quant-based Data Sufficiency?

   Because additional constraints (integer, positive, non-zero, ratio, or a quadratic factoring uniquely) can make fewer equations sufficient, while non-independent (redundant) equations can make 'two' equations effectively one—so the actual number of solutions must be verified.

7. In quant Data Sufficiency, why is a quadratic equation a classic trap for sufficiency?

   A quadratic typically yields two roots, so it gives two possible values and is often NOT sufficient—unless a constraint (e.g., the variable must be positive or an integer) eliminates one root, leaving a unique value.

8. What is reasoning-based Data Sufficiency and how does it differ from quant-based DS?

   Reasoning-based DS embeds the question in a logical/arrangement context (seating, ranking, blood relations, ordering) rather than numerical computation. Sufficiency depends on whether the clues uniquely fix the arrangement, not on solving equations.

9. In reasoning-based Data Sufficiency about arrangements, when is a statement sufficient?

   When the clue(s) reduce the possibilities to a single valid arrangement/answer; if more than one arrangement remains consistent with the clue, it is insufficient.

10. What is a common 'C-trap' in Data Sufficiency and how do you avoid it?

    The C-trap is the temptation to pick (C) (both needed) when in fact one statement alone is sufficient. Avoid it by rigorously testing each statement alone before combining.

11. What is the 'D-trap' in Data Sufficiency?

    The D-trap is wrongly selecting (D) (each alone sufficient) by overlooking that one statement actually permits multiple answers—always confirm each alone gives a truly unique result.

12. What is Data Comparison (quantitative comparison) and what are its standard answer choices?

    Data Comparison gives two quantities, Column A and Column B, and asks their relationship. Choices: (A) A is greater; (B) B is greater; (C) the two are equal; (D) the relationship cannot be determined from the given information.

13. In Data Comparison, when should you choose 'cannot be determined'?

    When the quantities depend on a variable whose range allows A>B in some cases and A<B (or A=B) in others—i.e., no single relationship holds for all permissible values.

14. What technique helps decide Data Comparison problems with variables, and what values are most useful to test?

    Plug-in/test values. Test strategic numbers—especially 0, 1, a negative number, and a fraction between 0 and 1—because they often flip the comparison and reveal 'cannot be determined'.

15. In Data Comparison, why is dividing or multiplying both columns sometimes dangerous?

    Multiplying or dividing both sides by a variable that could be negative or zero can reverse or invalidate the inequality; only operations that preserve the inequality (adding/subtracting the same quantity, or multiplying by a known positive) are safe.

16. What is the formula for average (arithmetic mean) commonly required across all DI sets?

    Average = (Sum of all observations) / (Number of observations).

17. In DI, how do you compute the ratio of two quantities and what does a ratio of 3:2 mean?

    Ratio = first quantity divided by second, expressed in lowest terms. A ratio of 3:2 means for every 3 units of the first there are 2 units of the second (first is 1.5 times the second).

18. What is the formula for percentage growth over multiple successive periods using a final and initial value?

    Total growth % = [(Final value − Initial value) / Initial value] × 100. For compound annual growth across n periods, use CAGR = [(Final/Initial)^(1/n) − 1] × 100.

19. In DI, how do you find the value of the 'others' or residual category in a pie chart or table?

    Subtract the sum of all explicitly given categories from the total (100% for percentages, or the stated grand total for absolute values); the remainder is the residual category.

20. When comparing growth across two bar-graph series, why can the series with the larger absolute increase still have a smaller percentage increase?

    Because percentage increase depends on the base (starting value). A large increase on a large base can be a smaller percentage than a small increase on a tiny base, so absolute and percentage rankings can differ.

21. In Data Sufficiency, how should you treat a statement that says 'x is a positive integer' combined with an equation?

    Treat the integer/positivity constraint as part of the information—it can eliminate negative or fractional roots and turn an otherwise insufficient statement into a sufficient one yielding a unique value.

22. What is the recommended strategy for choosing which DI set to attempt first under time pressure?

    Scan all sets and pick those with clear, well-labeled data and simple required calculations (tables/pie charts with round numbers) first; defer calculation-heavy or ambiguous caselet sets, prioritizing accuracy-per-minute.

23. In a line or bar graph, how do you identify the period of maximum percentage increase versus maximum absolute increase?

    Maximum absolute increase = the interval with the largest gap (difference) between consecutive values. Maximum percentage increase = the interval with the largest (difference / earlier value) ratio—compute the ratio, not just the gap.

24. For Data Sufficiency, summarize the decision flow as a quick mental checklist.

    1) Is Statement 1 alone sufficient? 2) Is Statement 2 alone sufficient? 3) If both alone sufficient → D. 4) If only one → A or B. 5) If neither alone, are they sufficient together? Yes → C, No → E.

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Data Interpretation & Data Sufficiency flashcards FAQ