LSAT Quiz: Evaluate The Argument

What this quiz covers

This quiz focuses on Evaluate The Argument, giving you a quick way to practice the rules, question types, and explanations that matter most for LSAT.

How to use this quiz

Try each quiz question before looking at the correct answer. Use the explanations to review missed ideas, then come back to similar questions until the pattern feels familiar.

Question 1

1. Did the restaurant change its table assignment procedure during the same period?
2. Did customers report technical difficulties accessing the QR-code menus?
3. When the restaurant used QR-code menus but kept the original menu size, did ordering times decrease? (correct answer)
4. Did the restaurant's average party size change after the menu changes?
5. Were the removed items among the restaurant's highest-profit dishes?

Explanation: The owner attributes the 6-minute reduction in ordering time entirely to the smaller menu, arguing that fewer choices speed decisions regardless of menu format. To evaluate this claim, we need to isolate the QR-code menu's effect from the menu-size effect—ideally through a test period with QR codes but the original menu size. Answer C provides exactly this scenario, asking whether QR-code menus with the original menu size also decreased ordering times, which would reveal the technology's independent contribution. Answer B about technical difficulties might explain some delays but doesn't help us determine whether the QR codes or the reduced menu size caused the overall improvement. When arguments attribute all improvement to one of two simultaneous changes, look for data from periods when the changes were implemented separately.

Question 2

1. Did the region where the test occurred also receive a special price promotion during the test quarter that could have affected purchasing behavior?
2. Were customer complaints about leaking typically higher in that region than in other regions before the new bottle and cap changes began?
3. Were the new bottle designs in the test region used with the old cap supplier's caps for any portion of the test? (correct answer)
4. How long has the new cap supplier been in business and does it hold industry certifications for its tighter tolerances?
5. Did the company change the wording on its customer-complaint form during the test quarter in ways that might discourage reporting?

Explanation: The managers claim the new cap supplier, not the bottle design, caused the 40% drop, because the new caps have tighter tolerances. But both changes happened at the same time, so the argument assumes the new bottles were always paired with the new caps. Knowing whether the new bottles were ever used with the OLD caps for part of the test is decisive: if leaking still dropped during that stretch, the design itself deserves credit, undermining the conclusion. This is the only option that separates the two overlapping variables. Asking about a price promotion targets sales volume, not leaking rates, so it doesn't help isolate what reduced complaints. Asking whether the region historically had more complaints gives background context but still can't tell you which of the two simultaneous changes drove the drop. Asking how long the supplier has operated or whether it holds certifications only reinforces that the caps are high quality—it never tests whether the bottle design also works. Asking about complaint-form wording raises a reporting-artifact possibility, but that would cast doubt on the 40% figure generally rather than distinguish the cap change from the design change, which is the argument's actual claim. Only isolating the design from the new caps lets you evaluate the managers' reasoning.

Question 3

1. Did the call center receive fewer calls overall during the month of the changes?
2. Did representatives receive additional training unrelated to the script during that month?
3. When the new script was piloted previously without the software upgrade, did call duration decrease? (correct answer)
4. How much did the software upgrade cost compared with the cost of script training?
5. Did customer satisfaction scores change during the same period?

Explanation: The operations director concludes that the software upgrade, not the new script, caused the 10% reduction in call duration because faster data retrieval shortens calls regardless of what representatives say. To evaluate this conclusion, we need to know whether the script alone can reduce call duration—ideally from a test without the software variable. Answer C directly provides this by asking about previous pilots where the script was tested without the software upgrade, revealing the script's independent effect on call duration. Answer A about overall call volume might affect average duration if different types of calls take different amounts of time, but it doesn't help separate the script's impact from the software's impact. Evaluation questions about attribution often hinge on finding data from controlled tests or historical periods when only one variable was different.

Question 4

1. Did customers report liking the slower-tempo music more than the previous music?
2. Were checkout staffing levels the same before and after the changes?
3. In stores that changed the music but not the layout, did average time in-store increase? (correct answer)
4. Did the chain introduce any new product lines during the same period?
5. How long did it take employees to learn the new aisle layout?

Explanation: The chain concludes that the new aisle layout, not the slower music, explains the 12% increase in customer time because layout changes force customers to walk more. To evaluate this, we need to isolate the music's effect from the layout's effect—ideally by examining stores that changed only one variable. Answer C provides exactly this control by asking about stores that changed music but not layout, which would reveal the music's independent contribution to increased shopping time. Answer A about customer preferences for the music might seem relevant, but liking the music doesn't necessarily translate to spending more time in the store, making it less useful for evaluating the causal claim. When multiple changes occur simultaneously, the most valuable evaluation information comes from situations where the changes can be examined separately.

Question 5

1. What other dietary habits did the participants have that might affect cognitive function? (correct answer)
2. How frequently did participants consume foods rich in omega-3s?
3. What specific memory tests were used to measure cognitive function in the study?
4. Were there any age-related differences in the study participants?
5. Did the study track participants' physical activity levels?

Explanation: The nutritionist concludes omega-3s are crucial for brain health based on correlation between omega-3 consumption and better memory test results, but acknowledges not considering other dietary factors. The correct answer (A) addresses this confounding variable gap—if participants with higher omega-3 intake also consumed more fruits, vegetables, whole grains, or avoided processed foods, then these broader dietary patterns rather than omega-3s specifically could explain the cognitive improvements. This challenges the single-nutrient causation claim. Answer (E) about physical activity levels introduces a different potential confounding variable but doesn't address the specifically acknowledged gap about other dietary factors. When arguments explicitly identify their analytical limitations, evaluation questions typically focus on exactly those acknowledged gaps.

Question 6

1. How much revenue does the library currently collect from late fees each year?
2. Do patrons in this community report avoiding borrowing items primarily because they worry about incurring late fees? (correct answer)
3. How many computers are available for public use at the library?
4. How many new books does the library typically acquire each month?
5. What are the library's current hours of operation on weekends?

Explanation: Evaluating this argument, the librarian's key claim is that ditching late fees will spike circulation by encouraging more borrowing. This depends on whether fees are the main deterrent or if other issues like selection limit use. Choice B captures this hinge by asking if patrons avoid borrowing due to fee worries, testing the causal mechanism. A distractor like A, on current fee revenue, adds fiscal context but doesn't determine if removal addresses the true barrier. Approach evaluate questions by identifying the option that challenges the motivational assumption at the decision point, revealing if the proposed fix targets the actual problem.

Question 7

1. How many employees participated in the trial department compared to other departments?
2. Were the projects completed during the trial comparable in difficulty and scope to the department's typical projects? (correct answer)
3. How long has the HR manager worked at the company?
4. What commuting distances do employees in the trial department typically have?
5. How many vacation days do employees receive under the current policy?

Explanation: This evaluate-the-argument question centers on the HR manager's claim that a four-day week will boost company-wide productivity, extrapolating from a trial's output rise. The validity hangs on whether that rise came from the schedule or anomalies like easier projects. Choice B directly interrogates this by asking if trial projects matched typical difficulty, isolating the schedule's role. Something tempting like A, about employee numbers, provides scale background but doesn't affect causation. In evaluate scenarios, select the variant that scrutinizes the evidence's representativeness at the decision point, differentiating real drivers from coincidental factors.

Question 8

1. Did the rail line change ticket prices at the same time it introduced the app?
2. What percentage of riders used the mobile app rather than paper tickets during the quarter?
3. On trains where inspector staffing did not increase, did fare disputes still decline after the app was introduced? (correct answer)
4. How many features did the app include besides mobile ticket purchasing?
5. Did the authority run advertising campaigns to promote the app to riders?

Explanation: The transit authority argues that increased inspector staffing, not the mobile app, caused the 35% drop in fare disputes because more inspectors deter fare evasion regardless of ticketing method. To evaluate this reasoning, we need to know whether the app reduces disputes even without increased inspection—essentially requiring a control group. Answer C provides this perfectly by asking about trains where inspector staffing didn't increase, which would isolate the app's independent effect on dispute reduction. Answer B about app usage percentage provides useful context but doesn't help separate the app's impact from increased inspection's impact on dispute rates. In evaluate questions involving policy changes with multiple simultaneous interventions, the most useful information comes from situations where only one intervention was implemented.

Question 9

1. What is the average size of green spaces in the areas studied?
2. Were the socioeconomic backgrounds of the areas with green spaces similar to those without? (correct answer)
3. How frequently do residents use the green spaces in their communities?
4. What specific types of crime were measured in the study?
5. Did the study consider the impact of green spaces on property values?

Explanation: This argument concludes that increasing green spaces will reduce crime, based on observed correlation between green spaces and lower crime rates, but acknowledges failing to account for socioeconomic factors. The correct answer (B) addresses this critical confounding variable—if areas with more green spaces also have higher income levels, better education, or more resources, then socioeconomic advantages rather than green spaces themselves could explain the lower crime rates. This challenges the causal inference from correlation. Answer (C) about green space usage frequency provides details about community engagement but doesn't address the fundamental question of whether green spaces or underlying socioeconomic differences drive the crime correlation. Evaluation questions often hinge on distinguishing correlation from causation by examining potential confounding variables.

Question 10

1. How many episodes were in the season that was released all at once?
2. Was the series that was released all at once significantly more popular than the service's typical series regardless of release strategy? (correct answer)
3. How many new subscribers joined the service during the month the series was released?
4. What time of day were the episodes made available to viewers on the release date?
5. How many employees worked on marketing the series during its release month?

Explanation: Here, the executive's central argument is that all-at-once releases will curb cancellations by fostering investment, drawn from one series' low churn. This hinges on whether that outcome stemmed from the strategy or the show's inherent appeal. Choice B addresses this by asking if the series was unusually popular anyway, checking for confounding popularity. Something like C, on new subscribers, adds growth details but doesn't influence if the strategy drove retention. With evaluate questions, target the variant that questions uniqueness at the decision point, separating method from exceptional cases.

Question 11

1. How old is the neighbor's furnace compared to the homeowner's furnace?
2. How much does a smart thermostat cost to purchase and install in the homeowner's area?
3. How often is the homeowner's house unoccupied for extended periods during winter weekdays? (correct answer)
4. What brand of smart thermostat did the neighbor install?
5. What is the average winter outdoor temperature in the homeowner's city?

Explanation: In evaluating this argument, the homeowner's central contention is that a smart thermostat will cut heating costs by 12% like the neighbor's, based on reducing waste during unoccupied times. This pivots on unknown facts about whether the home's schedule allows similar savings opportunities. Choice C precisely addresses this by asking how often the house is empty on winter weekdays, revealing potential for automated reductions. A trap like A, comparing furnace ages, offers equipment details but doesn't influence if usage patterns enable comparable efficiencies. For evaluate questions, hone in on the option that tests the applicability assumption at the decision point, distinguishing transferable benefits from situational mismatches.

Question 12

1. How much did the city spend installing sheltered stops and real-time arrival displays on the existing lines?
2. To what extent would riders on the existing lines switch to the new routes rather than taking additional bus trips overall? (correct answer)
3. How many buses per hour currently run on each of the two existing downtown lines?
4. What percentage of downtown commuters currently drive alone rather than carpool or use other transportation?
5. How satisfied are current bus riders with the cleanliness of buses on the existing lines?

Explanation: As an experienced LSAT tutor, let's dissect this evaluate-the-argument question where the council member's central claim is that expanding the bus network with amenities will boost overall ridership enough to cut car traffic. This hinges on unknown information about whether the new routes will attract genuinely new trips or just cannibalize existing ones, as mere shifting wouldn't increase totals. Choice B directly probes this hinge by asking to what extent current riders would switch versus taking more trips, revealing if the expansion adds net ridership. A tempting distractor like A, which asks about installation costs, offers financial background but doesn't impact whether ridership truly grows or just relocates. In evaluate questions, always seek the variant that targets the argument's decision point—here, the assumption that amenities drive additive, not substitutive, usage—to determine if the conclusion stands or falls.

Question 13

1. Did the company hire any new engineers during the timeframe in question?
2. Were releases delayed more often after the checklist was introduced?
3. Did teams that adopted the checklist earlier, before remote work began, show a similar reduction in defects? (correct answer)
4. How did employees rate their satisfaction with remote work in internal surveys?
5. Did the company change the programming languages used in its main product during that period?

Explanation: Management attributes the 15% reduction in software defects entirely to the new code-review checklist, dismissing any contribution from remote work. The key to evaluating this claim is finding data that separates the checklist's effect from remote work's effect—ideally from a time when only one change was in place. Answer C perfectly provides this by asking about teams that adopted the checklist before remote work began, which would show whether the checklist alone produces similar defect reductions. Answer D about employee satisfaction with remote work might correlate with code quality, but it doesn't directly test whether remote work or the checklist caused the improvement. In arguments that dismiss one simultaneous change in favor of another, look for historical or comparison data that isolates each factor's individual impact.

Question 14

1. During the period in question, did other nearby attractions also expand their weekend hours?
2. What proportion of weekend visitors attended the library specifically during the newly added hours? (correct answer)
3. How satisfied were library staff with the scheduling changes required by the extended hours?
4. Were the new weekend events advertised more heavily than the library's weekday programs?
5. Did the library's weekday visits change significantly during the same period?

Explanation: The administrators argue that extended hours didn't meaningfully affect weekend attendance because community events increased from 2 to 6 per month during the same period, and these events reliably draw large crowds. To evaluate this argument, we need to know whether the 18% increase in visits occurred specifically during the newly added hours or during the original hours when events might have been scheduled. Answer B asks exactly this—what proportion of weekend visitors came during the newly added hours—which would reveal whether extended hours contributed independently to the increase. Answer A about other attractions is tempting because it might explain general traffic patterns, but it doesn't help us separate the impact of extended hours from the impact of events at this specific library. When evaluating arguments that attribute an effect to one cause rather than another, focus on information that would help isolate each cause's individual contribution.

Question 15

1. Were any of the parks closed for renovations during the unusually rainy summer?
2. Did parks that did not receive the new grass also experience a similar percentage decline in watering costs? (correct answer)
3. How much did the town spend on planting and labor to install the new grass variety?
4. Did residents complain more or less about park conditions during the rainy summer?
5. How many different grass varieties were considered before selecting the new one?

Explanation: The department argues that the 30% above-average rainfall, not the new grass variety, explains the 20% reduction in watering costs because rain directly reduces watering needs. To evaluate this claim, we need a control group—parks that experienced the same unusual rainfall but kept the old grass. Answer B provides exactly this comparison by asking whether parks without the new grass also saw similar watering cost reductions, which would reveal if rainfall alone accounts for the savings or if the new grass contributed. Answer C about installation costs might be relevant for overall budget analysis but doesn't help determine what caused the reduction in watering costs specifically. When evaluating arguments about simultaneous environmental changes, the most useful information comes from control groups that experienced only one of the changes.

Question 16

1. Did teachers receive training on how to use the portal before the semester began?
2. Were there any changes in the types of homework assigned during the semester?
3. Did on-time submission rates increase for classes that used the portal but did not enforce the stricter late-work policy? (correct answer)
4. How many parents created accounts on the portal during the semester?
5. Did the district's overall enrollment change during the semester?

Explanation: The district attributes the improvement in on-time submissions entirely to the stricter late-work policy, dismissing the portal's contribution. To evaluate this claim, we need to know whether the portal improved submissions even without the stricter policy—essentially, we need a control group. Answer C provides exactly this by asking about classes that used the portal but didn't enforce the stricter policy, which would isolate the portal's independent effect. Answer B about types of homework might seem relevant since different assignments could affect submission rates, but it doesn't help us separate the portal's impact from the policy's impact. When an argument dismisses one cause in favor of another based on simultaneous implementation, look for information about situations where only one change occurred.

Question 17

1. Do universities and tech companies in urban areas collaborate more frequently than those in rural areas?
2. How does the presence of remote work options affect innovation in rural areas? (correct answer)
3. What percentage of successful startups in cities rely on online platforms for collaboration?
4. Were there any rural areas mentioned in the article as examples of innovation?
5. How many universities are typically found in urban areas compared to rural ones?

Explanation: The argument claims urban concentration is essential for innovation, using city-based startup examples as evidence, but acknowledges it doesn't address remote work and online collaboration. The correct answer (B) probes exactly this gap—if remote work enables significant innovation in rural areas, this would directly challenge the 'urban concentration is essential' conclusion. Modern technology could allow rural innovators to collaborate effectively without physical proximity to universities and tech companies. Answer (A) about university-tech company collaboration frequency provides context about urban advantages but doesn't address whether remote technology might overcome geographic barriers. When arguments ignore technological changes that could undermine their premises, evaluation questions should focus on those technological factors.

Question 18

1. How were productivity levels measured for the employees using the tool?
2. Was there a control group of employees who did not use the tool for comparison? (correct answer)
3. Did the employees receive any training on how to use the new software tool?
4. What types of tasks were included in the productivity measurement?
5. How did employee satisfaction with the tool influence its usage?

Explanation: The argument claims the software tool increased productivity by 20%, but explicitly notes the lack of a control group for comparison. This creates a fundamental experimental design flaw: without knowing how productivity changed among employees not using the tool, we cannot isolate the tool's effect from other factors like seasonal variations, company-wide initiatives, or general motivation changes. Answer (B) directly addresses this missing control group issue—the most critical information for evaluating causation. Answer (A) about measurement methodology provides technical detail but doesn't resolve whether the observed changes were specifically caused by the tool rather than external factors. In evaluation questions involving claimed causal effects, the presence or absence of proper control groups is typically the decisive validity factor.

Question 19

1. Were there significant changes in the birds' natural habitat during the study period? (correct answer)
2. How frequently were the bird populations monitored during the study?
3. What methods were used to count the bird population in the study?
4. Did the report include data on the birds' food supply over the two years?
5. Were there other conservation measures implemented alongside the hunting ban?

Explanation: The conservation group concludes the hunting ban caused bird population increase, observing 25% growth over two years, but acknowledges not considering other environmental changes during this period. The correct answer (A) addresses this critical alternative explanation gap—if the birds' natural habitat improved through restoration projects, climate changes, reduced pollution, or increased food sources, these factors rather than reduced hunting could explain population growth. Answer (E) about other conservation measures is tempting but focuses on additional human interventions rather than broader environmental changes that could independently boost population. When arguments attribute causation to a single intervention while acknowledging unexplored environmental factors, evaluation questions should probe those broader environmental possibilities that could provide alternative explanations.

Question 20

1. Did the remote workers in the survey have different job roles than those in the office? (correct answer)
2. How was employee engagement measured in the survey?
3. What percentage of the survey participants were new to remote work?
4. Did the survey take into account the size of the companies where participants worked?
5. How did the survey define 'higher satisfaction' among remote workers?

Explanation: This argument concludes remote work is the key to employee engagement based on higher reported satisfaction among remote workers, but acknowledges not addressing how job roles might differ between remote and office workers. The correct answer (A) probes this critical gap—if remote workers tend to have more senior positions, creative roles, or greater autonomy compared to office workers in routine positions, then job characteristics rather than work location could explain the higher engagement. This challenges the location-based causation claim. Answer (B) about measurement methodology provides technical detail but doesn't address whether observed differences stem from work location versus job role characteristics. Evaluation questions often focus on whether compared groups are truly comparable or systematically different in ways that could explain observed outcomes.