Writing LSAT without learning formal reasoning is like walking into a used car lot without knowing anything about cars. Unless you are familiar with common logical flaws, you won’t be able to spot them in the exam.
Many grammatical errors are so commonly made in everyday speech that we no longer recognize them as incorrect, even though they technically are. Logical flaws, or fallacies, are similar. In daily life we don’t always use perfect logic: we make assumptions, jump to conclusions, and read into things. Logical fallacies are specific cases of these errors in reasoning.
You don’t have to perfectly memorize every logical fallacy and it’s unnecessary to learn the Latin names; however, you do need to be able to identify incorrect reasoning when you see it on the test, and choose the answer choice that accurately describes the specific error being made.
1. Ad Hominem
Ad hominem means “to the man” and indicates an attack made upon a person rather than upon the statements that the person has made. It is logically incorrect to make conclusions about someone’s policies, actions, or ideas based on their character; likewise, it is incorrect to attack a person’s charcter in order to invalidate their policies, actions, or ideas.
The doctor is a hypocrite. Don’t trust his diagnosis.
Don’t vote for him to be president since he is an incorrigible womanizer.
Here’s more information about the ad hominem fallacy:
You can strengthen an ad hominem argument by showing that the personal characteristic is, in fact, relevant.
Don’t vote for him to be president since he is an incorrigible womanizer.
This argument can be strengthened by the statements:
- Numerous affairs could be a distraction from governance.
- Numerous affairs may make the president susceptible to blackmailing.
You can weaken an ad hominem argument by showing that the personal traits aren’t important.
Don’t hire him as your landscaper since he is an incorrigible womanizer.
This argument can be weakened by the statements:
- It is difficult to see how a landscaper’s personal life could have any impact on his performance.
- All of the landscaper’s clients are male, so the way in which he interacts with women is irrelevant.
2. Straw Man
In a straw man argument, the speaker gives the impression that he or she is refuting an argument made by an opponent. However, the argument being refuted does not represent the opponent’s true position. The arguer is “attacking a straw man”. For instance, a political candidate might argue against “letting all prisoners go free”, attributing that position to his or her opponent, when in fact the opponent simply favors a highly limited furlough system.
The below video captures the essence of Straw Man fallacy.
The Congressman wants to cut funding for the attack submarine program. I disagree entirely. I do not understand how he can be so irresponsible and leave us defenseless like that.
The congressman just argued for cutting the funding for a specific program. However, the other person is charging that the congressman is “leaving us defenseless”. Clearly, the other person is trying to refute an argument of a Straw Man, not a real person.
You can strengthen a Straw Man argument by showing that the understood argument is not an exaggerated version of the original argument.
The submarine program forms the foundation of our country’s defense system.
You can weaken a Straw Man argument by showing that the understood argument is really an exaggerated version of the original argument.
The assault on the Congressman’s character is far off base. The Congressman knows that the defense budget is finite and prefers spending scarce dollars on more effective defense systems that are unlike the already obsolete attack submarines.
3. The Fallacy of Faulty Analogy
Reasoning by analogy functions by comparing two similar things. Faulty Analogy arguments draw similarities between the things that are not relevant to the characteristic in the conclusion.
The logic behind analogies is this: All X does Y. This does Y. Therefore, this must also be an X. Here’s an example of a Faulty Analogy fallacy:
Ted and Jim excel at both football and basketball. Since Ted is also a track star, it is likely that Jim also excels at track.
In this example, a couple of similarities between Ted and Jim are taken as the basis for the inference that they share additional traits.
Here’s a video that explains, using examples, this fallacy (also called Weak Analogy Fallacy).
Strengthen: You can strengthen a faulty analogy argument by saying that the similarities are indeed relevant to the characteristic being concluded.
All three – football, basketball, and track – emphasize speed.
Weaken: You can weaken a faulty analogy argument by emphasizing the differences between the things being discussed in the analogy.
Ted plays point guard in basketball and wide receiver in football, two positions that emphasize speed. Jim plays center in basketball and nose tackle in football, two positions that emphasize size. Therefore, it is fallacious to suggest that Jim might excel in track in which speed is a great determinant of success.
Faulty Analogy Sample Question:
Long-distance runners sometimes get shin splints from over training. Shin splints are also common among freestyle skiers. Therefore, Freestyle skiers must also be guilty of over training.
Which of the following, if true, most weakens the conclusion drawn above?
- Sprinters are also prone to getting shin splints.
- Freestyle skiers often exhibit other signs of over training such as dehydration.
- Long-distance runners are less prone to long-term stress injuries.
- Freestyle skiers get shin splints from landing jumps incorrectly.
- Freestyle skiers, on average, train fewer hours than do long distance runners.
The passage tells us two facts, one about long-distance runners (they sometimes get shin splints from over training) and one about freestyle skiers (they also get shin splints). The conclusion, that freestyle skiers must also over train, depends on the faulty assumption that because runners’ shin splints are caused by over training, skiers’ must be as well. Choice A is irrelevant. Choice B strengthens the conclusion. Choice C is irrelevant.
Choice D attacks an assumption on which the conclusion depends. If skiers’ shin splints are not caused by over training, then it is not necessarily true that freestyle skiers are guilty of over training. This weakens the conclusion considerably. Choice E seems to weaken the conclusion by suggesting that freestyle skiers may be less likely to over train than runners, but it is, in fact, irrelevant. Choice D is the best answer.
4. The “After This, Therefore, Because of This” Fallacy
This is a causal fallacy in which something is associated with something else because of a mere proximity of time. This error is very common on the LSAT and it usually accompanies any chronological question (over years, days, etc). One often encounters people assuming that because one thing happened after another, the former must have caused the latter, as with:
The last thing I remember was a bus coming at me full speed. I am now in a hospital in a full body cast. The bus must have caused my injuries.
Ten minutes after walking into the auditorium, I began to feel sick to my stomach. There must have been something in the air in that building that caused my nausea.
The below video explains this fallacy with examples:
Strengthen: You could strengthen the second argument above by saying: the auditorium was later closed because of a gas leak, which supports the assumption that something was in the air.
Weaken: You could weaken the argument by targeting the assumption: Before going to the auditorium, he ate lunch at a restaurant that just reported a high level of food poisoning.
Let’s look at another example:
The stock market declined shortly after the election of the president, thus indicating the lack of confidence the business community has in the new administration.
This is typical of modern news reporting. The only evidence offered in this argument to support the claim that the decline in the stock market was caused by the election of the president is the fact that the election preceded the decline. Another factor could have been a collapse of a bank in Asia that had no relation to the election. The underlying assumption is that there are no other factors at work that could be the real cause.
Over the past three years, the crime rate in the city has steadily declined. Four years ago, a new mayor took office on a third party ticket whose platform included a tougher stance on crime and improved funding for after-school and other youth programs. Without this mayor’s leadership, it is certain that this positive change in the crime rate would never have occurred.
Which of the following statements, if true, would most weaken the argument above?
- In the first year the mayor was in office, the crime rate rose by 1.5%.
- Due to budget cuts, the mayor’s proposed funding for after-school and other youth programs was never implemented.
- Three years ago, a new chief of police was appointed who instituted foot patrols in high crime areas.
- The crime rate in neighboring cities has been on the rise for the past three years.
- The after-school programs had an even higher rate of attendance than was expected.
This question asks you to weaken the argument. The author writes that without the new mayor’s leadership, the recent decline in the crime rate would not have occurred. However, the only evidence we’re given is that the mayor’s platform included anti-crime programs that preceded the drop in crime. There are dozens of possible reasons for a decline in crime, therefore this argument isn’t very persuasive. To weaken the argument, find a statement that shows that the decline in the crime rate may have been caused by something other than the mayor’s taking office.
Choice C suggests that it could have been caused at least in part by the new chief of police, whose increase in officers patrolling by foot could very well have made a positive impact on the crime rate. Choice A is irrelevant; we’re only concerned with the past three years. Choice B fails to weaken the conclusion because it’s possible that while the youth programs were never implemented, other anti-crime programs were. Choice D is irrelevant. Choice E strengthens the argument. Choice C weakens the argument, and is the best answer.
5. The “All Things are Equal” Fallacy
Plus ça change, plus c’est la même chose
(The more things change, the more they remain the same.)
– Jean-Baptiste Alphonse Karr
This fallacy is committed when it is assumed, without justification, that background conditions have remained the same at different times/locations. In most instances, this is an unwarranted assumption for the simple reason that things rarely remain the same over extended periods of time, and things rarely remain same from place to place.
These questions can be easily spotted because they talk about “last year”, or some past time and try to create an analogy to predict future events. Over time, however, all sorts of dynamic factors change, so it is difficult to draw direct inferences from past events.
Ten years ago I got a 170 in the LSAT, so I expect to get the same score again.
The last year Democrat winner of the New Hampshire primary won the general election. This year, the winner of the New Hampshire primary will win the general election.
The assumption operative in this argument is that nothing has changed since the last primary. No evidence or justification is offered for this assumption.
6. Either / Or Thinking
This is the so-called “black or white” fallacy. Essentially, it says “Either you believe what I’m saying, or you must believe exactly the opposite.” Here is an example of the black or white fallacy:
Either you are with me or you are against me.
The argument above assumes that there are only two possible alternatives open to us. There is no room for a middle ground.
You can strengthen these arguments by showing that there, generally, is no middle ground. The problem is that most arguments do have a middle ground, meaning that this argument doesn’t work and is very often a fallacy.
7. Argument ad populum
This is argumentum ad populum, the belief that truth can be determined by, more or less, putting it to a vote.
A group of kindergartners are studying a frog, trying to determine its sex.
“I wonder if it’s a boy frog or a girl frog,” says one student.
“I know how we can tell!” pipes up another.
“All right, how?” asks the teacher, resigned to the worst.
Beams the child: “We can vote.”
Democracy is a very nice thing, but it doesn’t determine truth. Polls are good for telling you what people think, not whether those arguments are valid or not.
The video below explains this fallacy:
8. Slippery Slope
This argument assumes that just because things go badly, they will automatically get much, much worse. All graphs use by slippery slope look like hockey sticks.
The anti-terrorist laws that monitor international currency transfers, phone calls and emails are the first step to turning our fragile democracy into a new Nazi regime.
Although infringements of civil liberties are troublesome, it is another argument to suggest infringements of civil liberties will lead to a fascist dictatorship.
The video below explains this fallacy in detail:
9. The Fallacy of Equivocation
The Fallacy of Equivocation occurs when a word or phrase that has more than one meaning is employed in different meanings throughout the argument.
“Every society is, of course, repressive to some extent – as Sigmund Freud pointed out, repression is the price we pay for civilization.”
-John P. Roche- political columnist
In this example, the word repression is used in two completely different contexts. “Repression” in Freud’s mind meant restricting sexual and psychological desires. “Repression” in the second context does not mean repression of individual desires, but government restriction of individual liberties, such as that in a totalitarian state.
The video below explains this fallacy using different examples:
10. Non Sequitur
This means “does not follow,” which is short for: the conclusion does not follow from the premise. To say, “The house is white; therefore it must be big” is an example. Just because a house is white doesn’t mean that the house must be big. The below video explains this fallacy is an interesting way.
Now, let’s look at an example question that revolves around this fallacy.
Mayor of town T decided to lower sales tax in order to boost sales volume. He believes that lowering the tax will increase the sales tax generated since there will be much more total sales volume. The mayor wants to follow the example of town J, where such an experiment helped increase the budget twice in a three-year term.
Which of the following statements is the best proof the opponents of the mayor’s proposal can use in order to persuade the population of town T to not support this decision?
- Town J is located very close to the borders of other three states. The sales taxes in those other states are higher than in Town J’s state. This causes residents of other states to shop in town J, to save money. Town T is located far from any state border.
- Town T relies receives only a small portion of its tax receipts from sales taxes. Most taxes come from property taxes and this policy would have no impact on property tax returns.
- Town J has many more industrial plants that purchase raw materials from the town’s mines.
- This kind of experiment did not work in any other of the six towns that lowered sales tax.
- The mayor is corrupted by several groups of residents of town T. These groups are highly interested in lowering the sales tax because it would make them much richer.
This is a faulty comparison question where two towns are compared.
- This is a non sequitur trap choice. The issue of a city’s tax level has nothing to do with what states you are in. You could just travel from city to city (not state to state) since this is a city tax issue, not a state tax issue.
- Although the tax benefit may be small, this does not counter the Mayor’s argument that it would increase tax revenues.
- Out of scope.
- Correct. Six other examples where this idea didn’t work is a reasonable counterpoint.
- Out of scope.
There are lies, damn lies – and statistics.
To help you prepare for the many statistical reasoning questions that you will likely encounter on test day, we provide a primer on statistical reasoning similar to what you get in a college-level introductory statistics class.
1. The Biased Sample Fallacy
The Fallacy of the Biased Sample is committed whenever the data for a statistical inference is drawn from a sample that is not representative of the population under consideration. The data drawn and used to make a generalization is drawn from a group whose characteristics are not as the characteristics of the population. Here is an argument that commits the fallacy of the biased sample:
A recent study showed that over 60% of Oregon residents watched cartoons. Based on this study, executives at Cartoon Channel spent $10 million to expand their access to Oregonians, who appear to be avid fans of cartoons.
Note that this survey doesn’t say anything about the specific Oregon residents polled. Are they school children? The results seem to indicate so. A sample must be representative of the overall population that we want to study in order to make a general conclusion.
Here is another example:
In a recent survey conducted by Wall Street Weekly of its readers, 80% of the respondents indicated their strong disapproval of increased capital gains taxes. This survey clearly shows that increased capital gains taxes will be met with strong opposition from the electorate.
The data for the conclusion in this argument is drawn from a sample that is not representative of the entire electorate. The survey was conducted of people who invest, and not random members of the electorate. People who read about investing are more likely to have an opinion on the topic of taxes on investments than the population at large.
2. The Insufficient Sample Fallacy (Hasty Generalization/Sweeping Generalization)
The Fallacy of the Insufficient Sample is committed whenever an inadequate sample is used to justify the conclusion drawn. In a Biased Sample, people are pulled from a non-representative group, in an Insufficient Sample, not enough people are polled to make a statistically significant result.
I have worked with three people from New York City and found them to be obnoxious, pushy and rude. It is obvious that people from New York City have a bad attitude.
Observations of three people are not sufficient to support a conclusion about 10 million. Bad luck could account for meeting three bad people. Try this one:
After living and working in New York City for 12 years, I have met thousands of people and with very rare exception, I have found them to be obnoxious, pushy and rude. It is obvious that people from New York City have a bad attitude.
This latter argument is something to be taken more seriously given the larger pool from which the observation is drawn.
Here’s a video that explains this fallacy with more simple examples:
3. Correlation vs. Causation
A correlation is a statistical linking between two items that seems to be parallel. One of the LSAT’s “Greatest Hits” you see time and time again is the attempt to link up two separate items that seem to statistically correlate and then establish one of the two as the “cause”.
The relation between an association and a cause is difficult.
- Heavier people tend to be taller.
- Weight is correlated with height.
- Gaining weight will make you taller.
This argument assumes a relationship between correlated data and thus concludes that by changing one element, you can change the other.
Another obvious one:
- More fire trucks tend to be at more serious fires
- We can reduce the severity of fires by reducing the number of fire trucks.
Here is a more challenging example:
- Young people who watch more TV violence are more likely to engage in violence.
- The recent increase in TV violence is associated with an increase in violence society-wide.
- If children would watch less TV, they would be less violent.
This one seems intuitive enough and it’s the “sentimental favorite”, but the reality is that (3) can’t be proven from 1 and/or 2. You can’t assume that just because things correlate you can change one factor and it will automatically change the other. Children who watch large amounts of TV may have inattentive parents, and this may be the underlying hidden causal factor (not watching too much TV violence in itself). This argument could use more evidence, like a study showing that violent children are more successfully rehabilitated by cutting off violent shows.
Studies have shown that men aged 18-27 who have owned a pet for at least 2 years before marrying are 35% less likely to divorce. Researchers conclude that caring for a pet prepares men for long-term, healthy relationships in marriage.
Which of the following, if true, most strengthens the conclusion that men who have owned pets are prepared for healthy marriages?
- Studies have shown that pet ownership drastically reduces daily stress levels.
- Many successful marriages are based on emotional investment in a common interest, such as a pet.
- Many men who have been married for 25 years or more continue to own pets.
- Men who have not owned pets for at least two years before marrying are more likely to divorce.
- Men whose wives who owned a pet for at least two years are equally as unlikely to divorce.
Situation: Researchers have concluded that men who have owned a pet for at least 2 years are prepared for healthy marriages.
Reasoning: Which option most strengthens the conclusion? Researchers base their conclusion on an assumed connection between sustained care for a pet and care for a spouse. Men who care for pets before marriage, the argument runs, are also statistically more likely to sustain marriage relationships. The problem is that correlation doesn’t prove causality, so that link alone is not enough.
- While this may be true, it does not introduce additional evidence to support the conclusion.
- This option does not address the question of why men who own pets are less likely to divorce.
- The question concerns men who have owned pets before marrying, not after.
- Correct. This option provides additional evidence of a causal correlation between pet ownership and the likelihood of divorce.
- The question concerns men, not their wives.
The correct answer is D.
4. Confounding Factors (aka “Lurking Variables”)
A confounding factor is an additional factor that may be responsible for a correlation. “Con” is a latin root for “with”, so confounding means literally to found with.
Example 1: The Miracle Hospital
A sports injury treatment center in New York has the lowest rate of recovery for sports injuries. A treatment center in rural Pennsylvania has the highest and quickest recovery rate. If you have just been severely injured while playing softball, should you go to Pennsylvania?
In this example it appears pretty obvious that this hospital in New York is bad for your health. So you follow the statistics and go to Pennsylvania, right? The treatment center in New York is an option of last resort for serious sports injury patients like you. The Pennsylvania hospital is so poor that no one with a serious injury ever goes there. The hospital’s patients consist of those with minor injuries who recover quickly. The hidden confounding factor in this argument is that people with more severe injuries are choosing to go to New York, meaning that looking at their injuries is a biased sample.
Example 2: The Secret Conspiracy Against Men
At the University of California at Berkeley, the school had a much lower acceptance rate for men than for women and administrators could not determine why since the male applicants had higher SAT scores and grades.
Are the lower admissions rates of men a result of systematic bias?
Looking at the information, it appears that someone in the admissions department doesn’t like men and has been secretly rejecting their applications.
When looking more carefully at the data, men were much more likely to apply to the highly-competitive engineering program. The result was that men had lower rates of admission overall at the Berkeley. In non-engineering programs, however, the acceptance rates were identical. So gender played no direct role in admissions rates, the factor was the major chosen by the applicants.