If GMAT questions were dogs, Data Sufficiency questions would be pit bulls. Conventional wisdom says that they’re evil monsters but their owners and some in-the-inner-circle others love them more than any other type. With a face that only a mother could love, they look ugly, have a nasty bite if you cross them, but at least in the minds of some they’re fascinating, incredible creatures.
So let’s talk about Data Sufficiency – in this space, less about “how to do them” and more about “how to appreciate them”. Let’s learn to love Data Sufficiency by discussing a few of the reasons you probably hate them right now.
It’s too much like Simon Says
In a question like:
What is the value of x?
(1) x^2 = 16
(2) 3 < x < 5
it’s horrifically annoying that you’re punished for saying x = 4 in either statement because, yeah, I guess Simon didn’t say “positive” for statement 1, so it could be -4, and I guess Simon didn’t say “integer” for statement 2 so it could be 3.5, so that’s why the answer is C and not A, B, or D. But, come on – we’re adults here. Really? Simon Says?
But in a business context, considering all available options is huge. As we’ve discussed previously in this space, the “noninteger” in your market can be a lucrative opportunity or a devastating threat. The GMAT needs to determine which candidates are most likely to consider all the angles in business decision making.
Why does “no” mean “yes”?
In a question like:
Is x > 0
1) x^3 < 0
2) -x is positive
Both statements give you the answer “No”. So aren’t they wrong? Or is this another Simon Says deal where they’re trying to trick me?
Again, think of the business implications. In business, the answer “no” is just as valuable as the answer “yes”. When a team of executives gets together to decide “should we acquire this small competitor?”, the important thing isn’t that the answer be “yes, buy!”, but rather that a sound, definitive decision is made. In short, you have to embrace the bad decisions you don’t make as much as you celebrate that good decisions that you do make.
In the famous example of New Coke, in which Pepsi’s success in blind taste tests was the catalysts for Coke to change its century-old formula (to quick consumer backlash), a Data Sufficiency mindset would have been ideal. The correct answer to that question “should we scrap the formula that made us arguably the most popular brand in the world?” was clearly “no”. And the “given information” that people prefer Pepsi in a blind taste test was not sufficient at the time; further research was necessary to establish that Pepsi’s extra-sweet taste performed better in a one-sip test, but Coke actually performed better when people had an entire glass. Or that Coke’s traditional taste may not have tasted “better” to some, but its familiar flavor and branding were what people would buy, regardless.
In business, sometimes the answer “no” is the perfect answer – and so that’s the case with Data Sufficiency, as well.
Why is the statement sufficient even if there are multiple values for x?
In a question such as:
Is x > 5?
1) x^2 = 16
2) x^3 < 0
it’s often unsatisfying to students who don’t like statement 1 (but it could be either 4 or -4) or 2 (but it could be any negative number) even though each is sufficient. Again, the logic lies in business, where you often won’t have as much information as you’d like, but you will have enough information to make a decision. Here we’ve drawn a line in the sand at 5 – our job is to determine whether the value of x will ever be greater than 5. That’s akin to a business decision on whether to invest in a new product or factory – if the investment only makes sense at a return of >5, and our multiple different projections show that it won’t ever get there, we don’t invest. We don’t need to determine which projection is most trustworthy or which numbers, exactly, to use. If we know that this factory will never give us a satisfactory ROI, then we save ourselves the trouble of further research and study and we just make the decision now – no!
In this light, Data Sufficiency questions are really feasibility studies. Our goal is to answer the one question given – even if we cannot fill in all the details related to the situation, if we can answer that question we’ve done our job.
In summary, learn to play the Data Sufficiency game and you should find that your frustrations with the format are actually reasons to like (or at least better respect) these questions.