SNAP Data Interpretation and Data Sufficiency Syllabus

What is a mixed (combination) chart in Data Interpretation?

A combination chart presents two or more chart types together (e.g., a bar graph plus a line graph, or a table plus a pie chart) requiring you to relate data across both representations.

In a combination bar-and-line chart, how are the two data series typically distinguished?

Bars usually represent absolute quantities on the primary (left) axis, while the line represents a related measure such as a percentage, ratio, or growth rate, often on a secondary (right) axis.

Why are dual-axis combination charts a common source of error in DI?

Because each axis has its own scale; reading a value off the wrong axis gives a completely wrong number, so you must match each series to its correct axis.

What does a radar (spider) chart display?

A radar chart plots multiple variables on axes radiating from a common center, with each variable's value marked along its axis and the points joined to form a polygon, useful for comparing profiles across several dimensions.

In a radar chart, how do you compare two entities across all dimensions at once?

Compare the shapes/areas of their polygons; a point farther from the center means a higher value on that axis, and a larger overall polygon means generally higher values.

What three pieces of information can a bubble chart encode simultaneously?

The x-axis position (one variable), the y-axis position (a second variable), and the bubble size/area (a third variable).

In a bubble chart, what does the size of a bubble represent?

The bubble's area (size) is proportional to the magnitude of a third variable, so larger bubbles mean larger values of that variable.

What is Reasoning-Based DI?

Reasoning-Based DI gives data with missing or coded values and conditions/clues; you must use logical deduction (like in puzzles) to fill in the data before or while interpreting it.

How does Reasoning-Based DI differ from calculation-based DI?

Calculation-based DI focuses on arithmetic on fully given data, while reasoning-based DI requires logical deduction from clues to determine the data itself, blending puzzle-solving with interpretation.

What is the recommended strategy when starting a reasoning-based DI set with incomplete data?

Begin from the most definite clues (fixed or unique conditions), establish those values first, then use them to deduce remaining unknowns step by step.

What is approximation in Data Interpretation and why is it used?

Approximation is rounding figures to convenient values to speed up calculation; it is used because DI answer options are usually spread apart, so an estimate is enough to pick the correct option.

When approximating, why should you check how close the answer options are before rounding aggressively?

If options are far apart you can round heavily, but if options are close you must keep more precision, otherwise rounding may push you to the wrong nearby option.

A good shortcut for approximating large divisions in DI is what?

Round both numerator and denominator to easy figures (e.g., to the nearest 10, 100, or simple fraction) and simplify, then adjust the estimate slightly for the rounding done.

What is the shortcut to find x% of a number using the y% of x = x% of y rule?

a% of b equals b% of a. For example, 16% of 25 = 25% of 16 = 4, which is far easier to compute.

How do you quickly increase a quantity by a percentage using a multiplying factor?

Multiply by (1 + p/100). For example, increasing by 20% means multiplying by 1.20; decreasing by 20% means multiplying by 0.80.

What is the successive percentage change formula for two changes a% and b%?

Net change % = a + b + (a x b)/100, using the sign of each change (positive for increase, negative for decrease).

How can a ratio a:b be converted into each part's percentage of the whole?

Part's percentage = (its share / sum of ratio terms) x 100. For a:b, first part = a/(a+b) x 100 and second = b/(a+b) x 100.

What is a fast way to compare two fractions to decide which is larger in DI?

Use cross-multiplication: for a/b vs c/d, compare a x d with b x c; the fraction on the side of the larger product is greater (assuming positive denominators).

What is the percentage equivalent of the fraction 1/8?

1/8 = 12.5%.

What is the percentage equivalent of the fraction 1/6?

1/6 = 16.67% (approximately 16 and 2/3 percent).

What is the percentage equivalent of the fraction 1/3 and 2/3?

1/3 = 33.33% and 2/3 = 66.67% (approximately).

What is the percentage equivalent of the fraction 3/8?

3/8 = 37.5%.

What is the percentage equivalent of the fraction 5/6?

5/6 = 83.33% (approximately 83 and 1/3 percent).

Why is memorizing fraction-to-percentage conversions useful in DI?

Many DI percentages correspond to simple fractions; recognizing them (e.g., 12.5% = 1/8) lets you multiply by an easy fraction instead of doing long percentage calculations.

What is the basic structure of a Data Sufficiency question?

A Data Sufficiency question gives a question stem followed by two statements (I and II); you must determine whether the statements, individually or together, provide enough information to answer, without necessarily computing the final answer.

What is the standard five-option answer framework for Data Sufficiency questions?

(A) Statement I alone is sufficient but II alone is not; (B) Statement II alone is sufficient but I alone is not; (C) Both together are sufficient but neither alone is; (D) Each alone is sufficient; (E) Both together are still not sufficient.

In Data Sufficiency, why should you evaluate each statement independently before combining them?

Because the answer depends on individual sufficiency first; combining statements is only considered when neither alone is sufficient, and judging them together prematurely causes wrong answers.

In Quant-Based Data Sufficiency, what makes a statement 'sufficient'?

A statement is sufficient if it yields a single, unique, definite value or a definite yes/no answer to the question; if more than one possible answer remains, it is insufficient.

In Quant Data Sufficiency, why must you avoid actually solving for the final numeric value?

You only need to confirm a unique answer is determinable; fully solving wastes time, and the task is to judge sufficiency, not to produce the number.

In Reasoning-Based Data Sufficiency, how is sufficiency judged?

A statement (or combination) is sufficient if it logically pins down the required arrangement, relationship, or conclusion uniquely; if multiple valid configurations remain, it is insufficient.

In Data Sufficiency, what common trap involves a statement that merely restates the question or gives no new constraint?

A statement that repeats given information or adds no genuine constraint is insufficient; recognizing redundant data prevents wrongly marking it sufficient.