Doing the Most with the Least on Data Sufficiencyundefined
This tip for improving your GMAT score was provided by David Newland at Veritas Prep.
Your goal on data sufficiency is to achieve the greatest results with the information you are given.
For this reason, you should begin each data sufficiency question with the goal of choosing answer choice D—“Each statement ALONE is sufficient.” The answer to a data sufficiency question is not always going to be choice D. But in order to select another answer choice, you must first have ruled out the possibility that each statement alone is sufficient. That is because the most that you can possibly achieve on a problem is to have each statement be sufficient ALONE.
Exercise your Data Sufficiency Muscles. Think of data sufficiency as if you were attempting to lift a given weight. You first try to lift that weight with your right arm ALONE and then you try to lift it with your left arm ALONE. (This is like trying each data sufficiency statement independently to see if it is sufficient). Naturally, the best possible result is that you lift the weight with each arm independently—choice D. This shows the greatest strength on your part and it is “doing the most with the least.”
If you successfully lift the weight with one of your arms, but fail to lift it with the other arm, this is a great result as well. After all, you showed that you were strong enough to lift the weight with one arm alone. In data sufficiency terms, this is choice A or B; one of the statements was sufficient ALONE. Notice that you are not doing the most that you could, but you are still being as efficient as you can be.
Using Both Arms. The only time that you actually try to lift the weight with both arms (or in data sufficiency terms, try the statements TOGETHER) is if you fail to lift it with either arm ALONE. After all, if you can lift the weight with just one arm (or you find that one statement is sufficient by itself) why would you try both together? This simply does not make sense. Therefore you move to try BOTH TOGETHER (choice C) only if you have already eliminated all of the more efficient options (Choices A, B, and D).
Being “data efficient” is not only about the order of the answer choices, it is also about being efficient with your time and effort. By understanding that you do not need to try both statements together unless each of them is not sufficient alone you are being efficient. You are doing the most with the least time and effort.
You cannot lift the weight at all. Finally, choice E means you cannot lift the weight, even though you use both arms together. This is obviously the least-efficient option. You are now doing the least with the most. You might consider telling a small lie at this point, not wanting to admit that you could not lift the weight at all. However, this is where data sufficiency on the GMAT differs from weight lifting. Finding that both statements together are still not sufficient is no reason to be embarrassed. Choice E is a legitimate answer on data sufficiency and is correct about 20 percent of the time. The point is that you do not start with choice E in mind; you arrive at choice E after eliminating all of the more efficient options.
Becoming “Data Efficient.” How can you learn to do the most with the least on data sufficiency? The key is to have the proper mindset. Use each piece of information fully but never make assumptions. Here are some guiding principles:
• Do your work early. Always analyze the facts given in the question stem to see what you can learn, even before you move to the statements.
• Bring out the hidden facts. For example, if the question mentions numbers of pets or children, these must be integers and they cannot be negative.
• Focus on the question. The same information might be not sufficient if you need to get an exact value for x, but it could be sufficient if you only need to know whether x is greater than 5.
• Never assume that a given statement (or statements) is not sufficient. Do enough diagnosis or actual math that you are sure the statement is not sufficient. Assumptions often lead to data inefficiency.
So exercise your data sufficiency muscles and become more data efficient.
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