LSAT Strengthen and Weaken Questions

Strengthen and weaken questions ask how additional information affects an argument. A strengthen answer makes the reasoning more persuasive; a weaken answer makes it less persuasive. Neither task usually asks you to prove the conclusion true or false beyond doubt. The answer should change the strength of the inference from the evidence to the conclusion.

Find the conclusion and the gap

Before reading the choices, locate the conclusion and the evidence. Then ask what the evidence does not establish. If the argument is causal, the gap may be another explanation. If it generalizes from a sample, the gap may be whether the sample represents the larger group. If it predicts future results, the gap may be whether the conditions will remain similar.

Example: “The neighborhood added a protected bike lane, and cycling injuries fell the following year. The lane caused the reduction.” Evidence: injuries fell after the lane was installed. Conclusion: the lane caused the drop. Missing link: no other change explains the trend, and the injury measure covers comparable riders and periods. An answer that introduces a citywide helmet campaign could weaken; an answer that notes no other safety initiative changed could strengthen.

Write a short prediction such as “rule out another cause” or “show the survey group matches residents.” The prediction is a guide, not a constraint. A credited choice may strengthen the argument in a different way than you first anticipated, but it must still affect the conclusion's support.

Causal arguments

A causal argument claims that one factor produced an outcome. The fact that a cause came before an effect is not enough by itself. Ask whether a third factor changed, whether the outcome began before the cause, whether the proposed cause is present in a comparison group, and whether the effect could influence the alleged cause.

Four common lines of analysis are alternative causes, reverse causation, timing, and comparison. A competing cause can weaken the claim if it also explains the outcome. Reverse causation challenges the direction: perhaps the outcome prompted the intervention. Timing matters if the effect began before the cause. A control group can strengthen a claim when it is similar to the treated group but does not receive the intervention.

A new fact need not demonstrate certainty. If a city introduces a tutoring program and test scores rise, evidence that similar schools without the program saw no increase strengthens the causal inference. It does not rule out every possible difference, but it improves the comparison. An answer saying the program was popular is weaker unless popularity connects to participation or learning outcomes.

Generalizations and samples

A generalization uses observations about part of a group to make a claim about the whole group. Check sample size, selection, response rate, time period, and relevant differences. A survey of people who voluntarily attend a public hearing may not represent all residents. A test of one product model may not support a claim about every product in a line.

Original example: “A survey of 80 customers who visited the downtown branch found that 70 percent favor self-checkout. Therefore, most customers across the chain prefer self-checkout.” A strengthen choice might report that the survey randomly selected customers from every branch across different days and matched the chain's customer mix. A weakening choice might reveal that downtown customers are much younger and more likely to use self-checkout than customers elsewhere.

The sample does not always need to be random in a technical sense, but its method must support the scope of the conclusion. A choice that increases the sample size may help only if the added respondents are not selected in the same biased way. A huge online poll promoted by a store's fan community can still be unrepresentative.

Comparisons, predictions, and recommendations

A comparison argument may claim that one policy, product, or institution is better because it performed well in one setting. Strengthen by showing that the comparison is similar in relevant respects or that the measured difference is meaningful. Weaken by identifying an important difference, a hidden cost, or a measure that favors one side unfairly.

A prediction uses past or present evidence to forecast what will happen. Strengthen it by showing the relevant conditions are stable or that the causal mechanism will continue. Weaken it by identifying a coming change, a diminishing resource, or a reason the past trend will not persist. Do not confuse “this happened before” with “this must happen again.”

A recommendation depends on a goal and likely consequences. Suppose a library plans to stay open later because evening attendance has grown. A strengthening fact might show that most evening visitors are unable to use existing hours and that added staffing costs are manageable. A weakening fact might show that the late attendance comes from a one-time event and that a safer, cheaper branch already serves those users.

Original worked question

Argument: “A regional clinic piloted text reminders for appointments. During the pilot, missed appointments fell from 14 percent to 9 percent. The clinic should introduce reminders at all locations because they reduce missed visits.” Which fact most strengthens the conclusion?

Choice A: “Patients can choose whether to receive text messages.” This may affect accessibility, but it does not establish whether reminders reduced missed visits. Choice B: “The pilot period coincided with a change to appointment booking, which made cancellations easier.” This provides another explanation for the reduction and weakens the causal claim. Choice C: “Two similar clinics that did not use reminders had no comparable decline, while appointment policies stayed constant at the pilot clinic.” This strengthens by providing a comparison and reducing an alternative explanation. Choice D: “The reminder vendor also offers billing software.” This is irrelevant. Choice E: “Some patients prefer phone calls.” This could matter to implementation, but without evidence about missed visits it is less direct than C. C is the best answer because it supports the causal link and the proposed expansion.

Notice that choice C does not prove all locations will see the same result. It is the strongest support among the options. The question asks what strengthens the claim, not what establishes it with mathematical certainty. Choice E could matter if the argument depended on universal acceptance, but the stated conclusion concerns reduced missed visits and C addresses that outcome directly.

What does not count as strengthening

A fact can be true, relevant to the topic, and still do little for the argument. If a proposal concerns tutoring's effect on test scores, a fact about the program's logo is beside the point. If a claim is about causation, restating that the outcome occurred after the intervention does not address alternative causes. If an argument recommends a policy, showing that the policy is popular may not establish that it achieves the stated goal.

Do not evaluate answer choices by personal preference. A policy may sound sensible, a product may seem beneficial, or a study may resemble one you know. The test asks how a fact affects the given argument. An answer that makes the conclusion more emotionally appealing does not necessarily strengthen the reasoning.

Weaken without disproving

A weakening choice reduces support; it does not have to prove the conclusion false. If an argument says a new scheduling system caused shorter wait times, evidence that patient volume fell at the same time weakens the causal inference even if the system may still have helped. If a survey supports a conclusion about all voters, evidence that respondents came mostly from one party weakens the generalization even if the result happens to be true.

This distinction prevents you from rejecting a correct weaken answer for not being decisive. Ask whether the information gives a reason to have less confidence in the inference. Then compare its effect with the other choices. A modest but direct challenge can be better than a dramatic fact that does not touch the argument's key link.

Match the question wording

A stem may ask which statement, if true, most strengthens or weakens the argument. Treat each choice as true for the purpose of the question, even if it seems unlikely. Do not spend time disputing the premise. Some stems ask what would most help evaluate the reasoning; those questions call for information whose possible answers could push the argument in either direction, such as whether a control group had similar conditions.

A question asking what is required is different from one asking what strengthens. A necessary assumption must be true for the reasoning to work; a strengthening fact may be helpful but not indispensable. If the stem asks for a necessary assumption, use the negation test. If it asks for strengthen or weaken, judge the direction and degree of evidential impact.

A four-step approach

1. [object Object]
2. [object Object]
3. [object Object]
4. [object Object]

On review, write down why each attractive wrong answer fails. Maybe it addresses cost when the argument is about effectiveness, or offers a fact about one subgroup when the conclusion covers everyone. This specificity is more useful than simply recording “C was correct.”

Practice and timing

Start with untimed questions so you can learn to identify the conclusion and gap accurately. Then complete mixed sets where the task is not announced in advance. Once the reasoning is reliable, use timed sections to practice making decisions efficiently. The two LR sections are each 35 minutes, and all questions deserve an answer because there is no wrong-answer penalty.

If you miss many causal questions, practice writing the cause, effect, and alternative explanations before opening the choices. If sample questions cause trouble, ask what population the conclusion covers and how the sample was selected. If you select facts that merely sound relevant, add a step: state the conclusion and explain in one phrase how the fact changes support.

Evaluate the argument with useful information

Some stems ask which information would be most useful to evaluate an argument. Unlike a straight strengthen or weaken question, the best answer often identifies a fact whose possible values could support either side. Return to the gap and ask what unknown would change your confidence. In the bike-lane example, knowing whether injuries fell in nearby streets with no lane could help evaluate the claimed effect. A large drop there would suggest another citywide cause; no similar drop would make the lane explanation more plausible.

This is a practical check on whether an answer actually bears on the argument. A question about the lane's paint color may sound like a detail about the intervention, but it will not help unless the argument connects visibility to injury reduction. A question about the city's overall injury-reporting rules may be much more diagnostic because a reporting change could explain the measured outcome.

When two answers seem plausible, compare the scope of each. One may affect a minor premise while the other bears directly on the conclusion. Suppose a company claims a training course raised productivity across its workforce. Information that participants liked the instructor is less direct than evidence comparing similar trained and untrained teams over the same period. The latter addresses whether training explains the productivity difference.

The central habit is to evaluate the link, not the topic. Once you understand the gap, answer choices become easier to compare. A strong choice targets the missing connection; a weak choice may be interesting but leaves the reasoning exactly where it was.

Sources

• [object Object]
• [object Object]
• [object Object]