Flaws To Watch Out For

Front 1

Unjustified Assumptions

Back 1

The argument is based on a questionable assumption. That is, in order for the argument to be true, the author is depending on a premise that he or she didn’t write down and hasn’t proven. Thus, the conclusion can’t be validated unless the assumption(s) can be proved to be true.

Front 2

Skill & Will

Back 2

This argument assumes that people have the ability (skill) to do something or the motivation (will) to do it, when this has not been proven to be the case.

Front 3

Extreme Language

Back 3

The argument (usually the conclusion) uses language so extreme that the premises cannot justify the conclusion.

Front 4

Terms Too Vague

Back 4

Just as you are on the lookout for language that is too extreme, you’re also on the lookout for language that is too vague.

Front 5

Predicting the Future

Back 5

There’s nothing wrong with trying to predict the future, of course; it’s hard to run a government (or anything) without doing so. However, whenever an argument tries to predict the future, that’s your opportunity to point out that the future could actually turn out some other way. Anyone who tries to predict the future is automatically introducing a level of uncertainty into his or her argument.

Front 6

What’s Their Motivation?

Back 6

Whenever an argument is in the form of an advertisement or company announcement, you get to ask, “What’s the speaker’s motivation?” Is the speaker trying to promote a medication, make a company look good, sell something, or get elected?

Front 7

The Troubled Analogy

Back 7

There’s nothing wrong with a good analogy, of course, but analogies in GRE arguments are never good. Every time you make an analogy, you’re saying that something is like something else – except that it isn’t exactly like that, or you’d just be talking about the original topic. It’s your job to find and exploit the dissimilarities. It’s your job to point out that the arguer has not established enough similarities to make a good analogy between the two.

Front 8

Confusing Signs of a Thing for the Thing Itself

Back 8

Medical tests often report false positives, while failing to catch everyone who actually has the disease. The number of people who test positive for a disease is not identical to the number of people who have the disease. This effect is especially acute when people have an incentive (such as money) to over-report something, or an incentive (such as fear or laziness) to under-report something. A common variation on this problem assumes that, because a law exists, people must be following it. A law is not the same as compliance with a law.

Front 9

Short Term vs. Long Term

Back 9

Something that’s good in the short term, under certain circumstances (antibiotics, for instance) may not be good for you in the long term. Something that is a good idea in the short term (working all night to rescue people in emergency) might not even be possible in the long term. Similarly, something that’s good or possible long-term may not be good or possible short-term. Eating a carrot every day may be beneficial for your eyesight over many years, but it won’t help you pass your pilot’s exam next week.

Front 10

Sample Isn’t Representative

Back 10

If the GRE mentions a study, chances are that the sample is not representative. One in the argument pool refers to “French women in their eighties who were nursing-home residents.” Wow, what a very specific group! It’s your job to point out that what works for French female octogenarians might not work for non-French people, men, and people under 80.

Front 11

Sample Is Too Small

Back 11

If a GRE argument mentions how many people were in a study, it’s your job to stay that the study should have been bigger.

Front 12

No Control Group

Back 12

A good study should have a control group – that is, a group of people who are as similar as possible in every way, and differ from the rest of the group by only one variable. You can’t just give people a new medicine and measure whether their condition improves; you have to get a big enough group of people who meet certain conditions (such as having a particular illness at a particular stage), divide the group into two groups (balanced by gender, age, and a host of other factors), and give the drug to only one group. It’s important to make sure that the people receiving the drug do not just get better, but better than the other group. After all, what if it’s the sort of illness that goes away on its own? Maybe some outside force (the changing seasons?) will cause improvement in both groups. It’s your job to point out when a study lacks a control group, and what impact this might have on the study’s findings.

Front 13

The Ever-Changing Pool

Back 13

Most groups of people have a rotating cast of members. If a civic club voted in favor of something yesterday and against it 20 years ago, you wouldn’t automatically conclude that people in the club changed their minds over time; it’s pretty likely that the club includes different people than it did back then.

Front 14

Correlation Does Not Equal Causation

Back 14

Just because two things are happening at the same time doesn’t mean that one causes the other. One thing to consider is if the relationship was reversed (i.e., B is causing A as opposed to A causing B).

Front 15

Nothing Is Qualified

Back 15

Sometimes, you can get away without attaching numbers to things. Most people would be happy to be “healthier” or “richer,” even if you can’t measure that exactly. However, quantification (expressing things as numbers) becomes important when you try to argue something like “the eventual savings will outweigh the startup costs.” Be on the lookout for this type of situation – you are trying to compare two things that can be quantified, but aren’t.

Front 16

How Was It Before?

Back 16

Model Heidi Klum once responded to a fan’s question about getting back into shape after pregnancy with the question, “Well, how were you before?” It’s hard to judge the present, or predict the future, without information about the past.

Front 17

Alternative Cause

Back 17

Just because two things happened in a certain order doesn’t mean one caused the other. Could some outside force be the cause?

Front 18

Alike Doesn’t Mean Identical

Back 18

People who (or things that) are alike in some ways are undoubtedly different in others.

Front 19

Percent vs. Real Numbers (and Other Mathematical Confusion)

Back 19

If David pays 28% of his income in taxes and Marie pays 33% of her income in taxes, who pays more money to the government? Without knowing how much the two people make, it’s impossible to say. Don’t confuse percent with actual numbers of dollars, people, etc. In sum, if any numbers are presented in an Argument topic, see whether they are being cited in a logical way. This is the exact same reasoning about percents and percent change that you will need for the Data Interpretation part of the exam.