# Tips for Answering GMAT Data Sufficiency Questions

Following are some GMAT tips that apply specifically to Data Sufficiency questions — one of the two basic question formats you'll encounter during the exam's Quantitative section. (Also see this tutorial, which expounds on some of these tips.)

1. Memorize the five answer choices. They're the same for each and every Data Sufficiency question.

2. Analyze each numbered statement individually. Be careful not to carry over any information from one numbered statement to the other. (Making this mistake is remarkably easy, especially under time pressure and in a momentary lapse of concentration.)

3. Take special care if the question asks for a number value. If a question asks for a numerical value as opposed to a quantitative expression that includes variables, the question is answerable only if a numbered statement (1 or 2) yields one and only one possible numerical answer — not a range of values.

4. Use process of elimination to narrow the choices. If you can eliminate either choice (A) or (B), then you can also eliminate choice (D). If either numbered statement (1 or 2) alone suffices to answer the question, then you can eliminate answer choices (C) and (E).

5. Don't rely on your eye. In contrast to Problem Solving figures (visuals), Data Sufficiency figures are not necessarily drawn proportionately — unless a figure indicates explicitly that it is drawn to scale. Do NOT rely on your eye to measure angle sizes, line segment lengths, or areas. Instead, handle any Data Sufficiency question using your knowledge of mathematics along with the numbers provided.

6. Focus on concepts more than on numbers. Data Sufficiency questions are designed to test you primarily on quantitative concepts, not on your ability to manipulate numbers. (That's what Problem Solving questions are for.) So if you find yourself doing a lot of pencil work, you're probably on the wrong track.

7. Don't split hairs in analyzing story problems. As with Problem Solving questions, you should make reasonable real-world assumptions when it comes to Data Sufficiency questions cast in a real-world setting (so-called "story" problems). Don't split hairs by looking for subtle meanings or ambiguous language. The test makers are not out to trick you in this way.