Spotting Common Critical Reasoning Flaws on the GMAT

As we’ve seen, flaw questions on the GMAT follow predictable patterns: the flaw always in some way addresses either how the evidence is being interpreted to lead to the conclusion, or how the evidence was obtained. Let’s take a look at a few specific examples of common GMAT flaws.

Real Numbers v. Percentages

At Company X, 15% of the male executives took advantage of the corporate “Family Leave” program last year, enjoying 6 weeks of paid sabbatical after a birth or adoption in their family. Only 10% of female executives at Company X took advantage of the program. Therefore, it appears that more male executives than female executives at Company X are interested in programs that promote leave options for personal reasons.

There are actually two major flaws here, and we’ll look at the one that ISN’T the title of this sub-section first, just to get it out of the way: the evidence isn’t sufficient to support the conclusion. The conclusion is about the “interest” of executives in “programs that promote leave options for personal reasons,” but the evidence only discusses percentages of executives who were involved in one specific program.  Drawing a conclusion about programs in general based on that evidence is unsound. Also, participation alone is not necessarily indicative of the interest in the programs; maybe people ARE interested, but just haven’t had new babies in their families.
The more standard flaw here, though, is the “real numbers versus percentages” issue. Evidence is presented about percentages, and based on that evidence, a conclusion is drawn about quantity. But the conclusion is flawed: what if there are 100 male executives at Company X, and 200 female executives? In that case, there would be 15 male executives taking Family Leave, and 20 female executives; since 20 is clearly more than 15, the conclusion would not be properly drawn in that case.  We can see, then, that percentage evidence alone is not sufficient to support a conclusion regarding “real number” quantities.

Causation v. Correlation

Studies focusing on North Americans show that single men have an average lifespan of 72.1 years; married men have an average lifespan of 73.4 years. However, married women live an average of 75.4 years, 2.1 years less than single women, who have an average lifespan of 77.5 years. Researchers have concluded, based on the data from those studies, that husbands suck the life-force out of their wives, prolonging their own lives at the cost of their spouses’ longevity.

The issue here is that there is a correlation between two occurrences (the increased lifespans of married men and the decreased lifespans of married women) and the argument inappropriately assumes that there must be a causal link between those occurrences. Whenever you see an argument that presents two events—let’s call them X and Y– that occur together, and that then concludes that one event, X, must be causing the other event, Y, you should look for the possibility that Y in fact causes X, or that some outside factor, Z, is causing both X and Y.

Necessary v. Sufficient

In order to successfully navigate the Great Lakes Trail, a 47-mile hiking trail fraught with unsteady terrain, one must have a pair of supportive hiking boots. Drew recently purchased a pair of the most comfortable and supportive hiking boots on the market. Therefore, Drew should have no trouble navigating the Great Lakes Trail when he goes on his hiking trip next month.

Now, I know we’ve seen a lot of necessary/sufficient issues in past discussions, but in flaw questions, the application is simpler. The correctly-identified flaw here would be that the argument takes a condition that is necessary for achieving the goal of completing the hike, and has treated it as though it is sufficient. Good boots are necessary; without them, Drew couldn’t make the hike. But are they ENOUGH? What if Drew has a horrible virus and can’t even get out of bed, let alone go for a 47-mile hike? The boots are not, by themselves, SUFFICIENT to ensure success on the hike. As a reader, be on the lookout for arguments that don’t differentiate between necessary and sufficient conditions.