- What is the central argument supported by the data in the transit authority's study?
Answer: A relatively small shift in commuter behavior, from driving to using light rail, could lead to a substantial reduction in traffic congestion.
When you encounter reading comprehension questions about arguments and data, focus on identifying what the evidence actually supports rather than what it merely describes. Look for the logical connection between the facts presented and the conclusion they're meant to justify.
The study presents three key pieces of data: 75% of downtown commuters drive alone, average commute time is 45 minutes, and a 10% shift to light rail could reduce commute times by 15 minutes. The central argument connects these facts to show that a relatively modest behavioral change (10% switching from cars to rail) would produce a disproportionately large benefit (33% reduction in commute time). This demonstrates how small changes in commuter behavior could yield substantial improvements in traffic congestion.
Choice A correctly captures this argument about the outsized impact of a small shift in behavior. Choice B misinterprets the data by suggesting the study argues against rail expansion, when it actually supports it. Choice C exaggerates the study's position—nowhere does it claim forcing 75% of commuters to use transit is the "only way" to solve traffic problems. Choice D incorrectly implies causation that isn't established; the study shows correlation between solo driving and long commutes but doesn't claim solo drivers are the sole cause of the 45-minute average.
Study tip: In argument analysis questions, distinguish between what data describes versus what it's used to prove. The central argument is typically the "so what?"—the conclusion the author wants you to draw from the evidence, not just a restatement of the facts themselves.
- What conclusion can be drawn by integrating the long-term erosion rate with the most recent data?
Answer: The rate of shoreline erosion can be significantly accelerated by individual severe weather events, exceeding the long-term average.
When you encounter questions about data analysis and drawing conclusions, focus on what the evidence actually supports rather than making extreme assumptions. You need to integrate multiple data points to reach a reasonable conclusion.
The passage gives you two key pieces of information: a long-term average erosion rate of 2 feet per year over a decade, and a recent single-year measurement of 9 feet of erosion that coincided with three major storms. The correct answer is C because it logically connects these data points. The recent 9-foot erosion (which is 4.5 times the average) occurred during an unusually severe weather year, demonstrating that extreme weather events can dramatically accelerate erosion beyond typical rates.
Answer A is wrong because one year of unusual data doesn't invalidate a decade-long trend—both measurements can be accurate for their respective time periods. Answer B makes an unsupported leap by assuming the 9-foot rate will continue permanently, ignoring that this year had exceptional storm activity. Answer D incorrectly suggests the shoreline will naturally recover the lost land, which contradicts how coastal erosion actually works—eroded shoreline doesn't typically regenerate on its own.
The key trap here is confusing correlation with permanent change. Just because one severe year produced extreme erosion doesn't mean that rate is the new normal. Look for answer choices that reasonably explain the relationship between the data points without making extreme predictions about future trends. Always ask yourself: "What does this evidence actually prove, not what might it mean in the worst-case scenario?"
- How does the study data relate to the article's main argument?
Answer: The data offers some support for the argument by showing a higher average logic score among students who studied a foreign language.
When you encounter questions about how evidence relates to an argument, focus on whether the data actually supports, contradicts, or proves the claim being made. The key is distinguishing between correlation, causation, and proof.
The article argues that bilingualism improves cognitive function, and the study provides relevant evidence: students who studied a foreign language for three years averaged 88 on a logic test, while those with no foreign language study averaged 82. This 6-point difference shows a correlation between language study and higher logic scores, which aligns with the argument about cognitive benefits. The data doesn't prove causation definitively, but it does offer meaningful support for the claim.
Answer D correctly identifies this relationship—the data supports the argument by showing higher logic scores among language learners.
Answer A incorrectly assumes reverse causation without evidence. The passage doesn't suggest that logic-oriented students self-select into language classes. Answer B dismisses the findings by claiming the 6-point difference isn't "statistically significant," but the passage doesn't provide information about statistical significance, and a 6-point difference on a 100-point scale represents a notable gap. Answer C overstates the evidence by claiming the data "proves" language study is the "only way" to achieve high scores—this goes far beyond what the data actually shows.
Remember: when evaluating how evidence relates to an argument, look for words like "supports," "suggests," or "indicates" rather than "proves." Most real-world studies provide support for theories rather than definitive proof.
- What does the data in the report suggest about the nation's energy transition?
Answer: Despite solar energy becoming more affordable, the nation's reliance on fossil fuels remains overwhelmingly high.
When analyzing data-based reading passages, you need to carefully distinguish between what the statistics actually show versus what might be assumed or projected. This question tests your ability to draw accurate conclusions from numerical evidence.
The passage presents three key data points: solar panel costs dropped 70%, fossil fuels still represent 79% of energy consumption, and renewables account for only 12%. The correct interpretation is that despite solar energy becoming more affordable, the nation's reliance on fossil fuels remains overwhelmingly high (C). The word "despite" perfectly captures the contrast between falling costs and continued fossil fuel dominance.
Let's examine why the other choices misread the data. Choice A claims solar has become "the dominant source of energy," but 12% renewable energy (which includes all renewables, not just solar) clearly contradicts this. Choice B states that "high cost is the only reason" for low renewable adoption, but since costs have actually decreased significantly, this explanation doesn't match the evidence. Choice D predicts that renewable use "will soon surpass" fossil fuels, but the passage provides no timeline or projection data—it only shows the current situation where fossil fuels still dominate at 79%.
The key trap here is confusing correlation with causation or making predictions beyond what the data supports. Remember: on GED reading questions, stick closely to what the passage explicitly states or directly implies. Avoid answers that make unsupported leaps, use absolute language when the data shows exceptions, or predict future outcomes from current statistics alone.
- Based on the study's data, what is the most accurate evaluation of the new farming technique for corn?
Answer: The technique provides a significant benefit in water conservation but results in only a modest increase in crop production.
When analyzing data from scientific studies, you need to carefully examine what the numbers actually show rather than making assumptions about their significance. This question tests your ability to interpret quantitative data objectively.
The study presents two key findings: precision irrigation used 20% less water while producing only 5% higher crop yields. Answer A correctly captures both aspects - it acknowledges the substantial water conservation (20% is significant) while accurately describing the yield increase as modest (5% is a small improvement). This balanced interpretation reflects what the data actually demonstrates.
Answer B incorrectly assumes that a 5% yield increase is automatically a "failure." The passage doesn't provide information about costs or investment returns, so you can't conclude the technique failed based solely on the yield percentage. Answer C completely misreads the data, claiming a "massive 20% increase in crop yields" when 20% refers to water savings, not yield improvement. This represents a classic data confusion trap. Answer D contains two errors: it calls 5% "negligible" (which is subjective and unsupported) and suggests the technique is "harmful" when the data shows a positive, albeit small, yield increase.
Strategy tip: On GED reading questions involving data, always match percentages to their correct categories and avoid inserting value judgments that aren't supported by the text. Words like "significant," "modest," "massive," and "negligible" should align with the actual magnitude of the numbers presented, not assumptions about what those numbers should mean.
- What can be concluded about the initiative's impact based on this analysis?
Answer: The initiative successfully encouraged new businesses to open, but its effect on filling all empty storefronts has been modest.
When analyzing data about policy initiatives, you need to distinguish between what the evidence directly shows versus what it doesn't prove. This question tests your ability to draw reasonable conclusions without overreaching.
The data shows two clear facts: new business openings increased 25%, and vacancy rates dropped modestly from 15% to 13%. Answer D correctly captures both pieces of evidence—the initiative did encourage new businesses (25% increase) but had only a modest effect on overall vacancies (2-point drop).
Let's examine why the other options fail. Answer A claims every new business "remains in operation," but the passage gives no information about business survival rates—this goes far beyond what we can conclude. Answer B calls the program a "complete failure," which ignores the significant 25% increase in new businesses; just because vacancy rates didn't drop dramatically doesn't negate the positive business growth. Answer C assumes direct causation between the 25% business increase and the 2% vacancy decrease, but correlation doesn't equal causation—other factors could explain the vacancy change.
Answer D avoids these traps by acknowledging both the success (new business growth) and the limitation (modest vacancy improvement) without making unsupported claims about causation or outcomes the data doesn't address.
On GED reasoning questions involving data analysis, always stick to what the evidence actually supports. Avoid answers that make absolute claims ("every," "complete failure") or assume direct cause-and-effect relationships unless the passage explicitly establishes them.
- What does the survey data suggest about the nature of volunteerism?
Answer: The way people volunteer may be shifting from formal organizational roles to more informal, community-based helping.
When analyzing survey data, you need to look at the complete picture rather than focusing on isolated statistics. This question tests your ability to synthesize multiple data points and draw reasonable conclusions about trends.
The survey reveals two contrasting trends: formal volunteering dropped from 28% to 24%, while informal volunteering rose from 35% to 45%. Rather than indicating an overall decline in civic spirit, this data suggests people are simply changing how they help others. The correct answer is D because it accurately reflects both trends - people may be moving away from structured organizational volunteering toward more personal, community-based helping.
Answer A is wrong because it ignores the significant increase in informal volunteering and incorrectly claims there's a "lack of interest in informal helping." Answer B makes an unsupported leap by assuming the decline in formal volunteering reflects organizational ineffectiveness - the data doesn't provide evidence about why formal volunteering decreased. Answer C goes too far in the opposite direction, claiming the non-profit's concerns are "completely unfounded" when there genuinely is a decline in formal volunteering that could impact organizational capacity.
When reading data analysis questions on the GED, avoid extreme interpretations. Look for answer choices that acknowledge all the evidence presented, not just the pieces that support a particular narrative. The most defensible conclusions usually recognize complexity rather than painting simple pictures of total success or failure.
- Which conclusion is supported by the shelter's data in the context of its campaign message?
Answer: The campaign is accurate, as the vast majority of dogs (360 out of 400, or 90%) were adoptable and left the shelter alive.
When you encounter questions that ask you to evaluate whether data supports a campaign's claims, focus on what the numbers actually show versus what each answer choice suggests they mean.
Let's analyze the shelter's data: Out of 400 dogs, 320 were adopted and 40 were returned to owners—that's 360 dogs (90%) who were healthy enough to leave the shelter alive. Only 40 dogs (10%) required euthanization due to severe, untreatable issues. This directly supports the campaign's message that "many healthy, adoptable pets are in need of homes."
Answer choice C correctly identifies this: 90% of the dogs were indeed adoptable and successfully placed, which validates the campaign's core message.
Answer A commits a logical error by calling the campaign "misleading" simply because 10% of dogs weren't adoptable. A 90% success rate actually strengthens the campaign's credibility—no shelter can save every animal, especially those with severe health issues.
Answer B incorrectly frames the 40 euthanized dogs as a complete "failure." In animal welfare, some euthanization is unfortunately necessary for animals suffering from untreatable conditions. The 90% save rate represents success, not failure.
Answer D makes an absolute statement ("the only way") that isn't supported by the data and goes beyond what the passage discusses.
Study tip: On GED reasoning questions involving data interpretation, always calculate the actual percentages and ask yourself: "Do these numbers support or contradict the main claim?" Don't let emotionally charged language in wrong answers distract you from what the data objectively shows.
- Which statement best summarizes the connection between the behavior and the belief reported in the survey?
Answer: The majority of young adults use their smartphones while socializing, and a majority also believe this behavior is harmful to their relationships.
When you encounter questions about survey data and statistics, focus on what the numbers actually say versus what they might imply. This question tests your ability to accurately summarize statistical findings without making unsupported leaps.
The survey presents two key findings: 85% of respondents use smartphones while socializing, and 60% believe this behavior negatively affects relationships. Answer B correctly captures both statistics - that a majority engage in the behavior (85% is clearly a majority) and a majority find it problematic (60% is also a majority, though smaller).
Answer A makes an absolute claim that isn't supported - the data shows 60%, not 100%, believe there are negative effects. The word "all" is a red flag that should make you check whether the data truly supports such a sweeping statement.
Answer C incorrectly assumes the two groups don't overlap. The survey doesn't tell us whether the 60% who see negative effects are among the 85% who use phones while socializing - these could be overlapping or separate groups.
Answer D contradicts the passage entirely. It claims smartphone use leads to "stronger relationships for 85% of young adults," but the passage states that 60% believe the habit has a negative effect on relationships.
Study tip: In data interpretation questions, stick to what the numbers explicitly show. Watch for answer choices that use absolutes like "all" or "only" - these often misrepresent survey findings that deal in percentages and majorities.
- What is the most accurate interpretation of the campaign's performance data?
Answer: The campaign was successful in reaching a large audience, but it was less effective than average at inspiring user interaction.
When analyzing data, you need to distinguish between different metrics and avoid drawing conclusions that go beyond what the data actually shows. This question tests your ability to interpret marketing data objectively without making unsupported inferences.
The campaign had two measurable outcomes: reach (500,000 unique users) and engagement rate (1.5%). The reach indicates the campaign successfully achieved broad awareness, which was the stated goal. However, the 1.5% engagement rate falls below the 3% industry average, suggesting the content was less compelling at motivating user interaction. Answer D accurately captures both aspects—acknowledging the successful reach while noting the below-average engagement performance.
Answer A incorrectly assumes that low engagement means users disliked the product. In reality, most social media users scroll past content without engaging, regardless of their opinion. Non-engagement doesn't equal dislike. Answer B uses extreme language ("complete failure") that ignores the successful reach aspect. While the engagement was below average, the campaign did achieve its primary goal of broad awareness. Answer C confuses reach with popularity—just because 500,000 people saw the posts doesn't mean they found the product appealing or would purchase it.
When interpreting data on the GED, watch for answer choices that make extreme claims or draw conclusions beyond what the data supports. Look for balanced interpretations that acknowledge multiple dimensions of performance rather than oversimplifying complex results into absolute success or failure.
- How does the pilot program data support the principal's claim?
Answer: The data shows that students in the tutoring program were three-and-a-half times more likely to improve their GPA compared to the general student population.
When you encounter data comparison questions, focus on analyzing the relationships between different groups and what the numbers actually reveal about effectiveness.
The pilot program data strongly supports the principal's claim by showing a dramatic difference in outcomes. Students who attended tutoring had a 70% improvement rate (35 out of 50), while the general population had only a 20% improvement rate (240 out of 1,200). To find how much more effective tutoring was, divide 70% by 20% to get 3.5 times more likely to improve. This substantial difference suggests the targeted support is indeed making a significant difference, exactly as the principal claimed.
Choice A incorrectly focuses on the 15 students who didn't improve, ignoring that 70% success is actually quite strong and much better than the baseline. Choice B dismisses the data due to sample size, but 50 participants is sufficient to show a meaningful pattern, especially when the difference is this large. Choice D makes an extreme claim that tutoring is the "only" effective method, but the data only shows tutoring is more effective than average—it doesn't prove other methods don't work.
When analyzing pilot program data on the GED, look for comparative effectiveness rather than absolute perfection. A 70% success rate might seem imperfect until you compare it to the 20% baseline. Always calculate the relative improvement (3.5 times more likely) to understand the true impact, and avoid answer choices that use absolute language like "only" or "proves" unless the data genuinely supports such strong conclusions.
- How does the city's tax data support the Tourism Board's claim about the festival?
Answer: The data shows that hotel tax revenue during the festival month was more than triple the average of other months, suggesting a large economic impact.
When you encounter questions about data analysis and claims, focus on how well the evidence supports the specific argument being made. Here, you need to evaluate whether tax data backs up the Tourism Board's claim that the festival drives the local economy.
Let's examine what the data shows. Hotel tax revenue in July (festival month) was $500,000, while the average for other months was $150,000. This means July's revenue was more than triple the typical month ($500,000 ÷ $150,000 = 3.33), representing a dramatic spike that coincides with the festival. This substantial increase strongly supports the claim that the festival creates significant economic activity.
Looking at why the other choices are flawed: Choice A misinterprets the data by suggesting the festival had a negative impact simply because other months had lower revenue—but those other months establish the baseline that makes July's spike so notable. Choice B makes an extreme claim that the 20,000 visitors were the "only" source of hotel tax revenue all year, which the data doesn't support since other months also generated substantial revenue. Choice C dismisses the data as irrelevant because it only covers hotel taxes, but hotel occupancy is actually a strong indicator of tourism's economic impact, making this data quite relevant to the claim.
Remember that when evaluating whether data supports a claim, look for patterns that align with the argument. A dramatic increase in economic indicators (like tax revenue) that coincides with an event strongly suggests that event is having the claimed impact.
- Which statement best synthesizes the information presented in the article?
Answer: Although most adults use online sources for news, there is significant distrust regarding the reliability of information on social media.
When you encounter synthesis questions on the GED, you need to combine multiple pieces of information from a passage to form a complete, accurate understanding. Don't just focus on isolated statistics—look at how all the data points work together.
The passage presents two key findings: 55% of adults get news primarily from online sources, but only 20% trust social media information. The correct synthesis acknowledges both trends without overstating what the data shows. Choice D captures this balance perfectly—it recognizes that online news dominates while also noting the trust issues with social media specifically.
Choice A misinterprets the data by claiming online news is "less popular" than print journalism, when the passage clearly states 55% use online sources primarily. It also incorrectly applies the 20% social media distrust to all online news.
Choice B makes an extreme claim that the decline is "solely due" to overwhelming trust in online news, which contradicts the trust concerns mentioned in the passage.
Choice C draws an unsupported conclusion about print journalism's trustworthiness. The passage discusses print's decline and online distrust separately—you can't logically connect them to conclude that print is no longer trusted.
For synthesis questions, avoid answer choices that overstate conclusions, ignore contradictory evidence, or make claims the passage doesn't support. The best synthesis acknowledges complexities and apparent contradictions in the data rather than forcing a oversimplified interpretation.
- Based on the information in the article, which conclusion about the two services is most accurate?
Answer: TuneNow has a larger number of paying subscribers than StreamIt, despite having a smaller total user base.
When analyzing business data, you need to distinguish between total numbers and meaningful comparisons. This question tests your ability to calculate and compare specific metrics rather than just accepting surface-level statistics.
Let's work through the math to find each service's paying subscribers. StreamIt has 150 million total users, but only 40% are paying subscribers: 150 million×0.40=60 million paying subscribers. TuneNow requires all users to pay, so all 100 million users are paying subscribers. Therefore, TuneNow has 100 million paying subscribers compared to StreamIt's 60 million, making answer choice B correct.
Now let's examine why the other options fail. Choice A assumes that more total subscribers automatically means greater financial success, but it ignores that 60% of StreamIt's users generate no subscription revenue. Choice C makes a value judgment about which business model is "superior" based solely on user attraction, not financial performance or sustainability. Choice D incorrectly claims both services have equal paying subscribers—we just calculated that TuneNow has 40 million more paying customers than StreamIt.
When you encounter data comparison questions on the GED, always calculate the specific metrics being compared rather than relying on the most obvious numbers presented. Raw totals can be misleading—dig deeper to find the relevant subset of data that actually answers the question. - Which statement best reflects the relationship between the report's argument and the sales data?
Answer: The data supports the argument that experiential offerings are a promising growth area for physical stores, despite the dominance of e-commerce.
When you encounter questions asking about the relationship between an argument and supporting data, focus on whether the evidence actually supports the claim being made, and be careful not to overstate what the data proves.
The report argues that physical stores can survive by offering unique customer experiences. Let's see how the sales data relates to this argument. While online retailers captured 90% of overall retail growth (showing e-commerce dominance), the experiential category in brick-and-mortar stores grew by 15%. This data directly supports the argument by showing that the specific strategy mentioned—unique experiences—is indeed a growth area for physical stores, even in a market dominated by online sales.
Choice A correctly identifies this relationship: the data supports the argument about experiential offerings being promising, while acknowledging e-commerce's overall dominance. Choice B misses the key point by focusing only on the negative (online dominance) while ignoring the positive evidence about experiential growth. Choice C dramatically overstates what the data shows—15% growth in one category doesn't prove overall profitability superiority. Choice D makes an unsupported prediction about online retailers becoming "obsolete," which contradicts the data showing their continued dominance.
Remember that on reasoning questions, the correct answer will accurately reflect what the evidence shows without overstating or understating the relationship. Look for answers that acknowledge both the strengths and limitations of the data presented.
- Based on the fitness center's goal and the data provided, what is the most accurate assessment of the new HIIT class?
Answer: The class met its retention goal and was perceived as highly effective by those who participated consistently.
When you encounter data analysis questions, focus on identifying the specific criteria being evaluated and whether the evidence supports meeting those criteria.
The fitness center set a clear goal: retain at least 50% of participants after the first month. Let's check if this was met. Of the 120 members who signed up, 72 attended regularly for the entire month. This gives us a retention rate of 72/120 = 60%, which exceeds the 50% target. Additionally, 90% of those who stayed reported significant health improvements, indicating the class was effective for consistent participants.
Choice A focuses only on the 90% satisfaction rate among regular attendees, ignoring whether the retention goal was actually met. This represents incomplete analysis of the given criteria.
Choice C incorrectly concludes the class failed because "only" 72 out of 120 continued. This shows a misunderstanding of percentages—72 out of 120 is actually 60%, which surpasses the 50% goal.
Choice D commits a mathematical error by saying "nearly 40%" stopped attending. Actually, 48 out of 120 (40%) stopped attending, meaning 60% stayed. This choice also ignores that some attrition is normal and expected.
Choice B correctly recognizes both key points: the retention goal was met (60% > 50%) and the class was effective for those who participated consistently (90% reported improvements).
Study tip: In data analysis questions, always calculate the actual percentages and compare them directly to stated goals. Don't let words like "only" or "nearly" mislead you—do the math yourself.
- What do the survey results suggest about the campaign's effectiveness?
Answer: The campaign successfully changed behavior by getting more people to read labels, but this had only a small impact on actual sugar intake.
When you encounter questions about campaign or program effectiveness, look beyond simple success-or-failure thinking. Real-world interventions often have complex, partial results that require careful interpretation of multiple data points.
The survey results show two key findings: 60% of people changed their behavior by reading labels more frequently, and average sugar consumption dropped from 77 to 74 grams. This represents a successful behavioral change with modest practical impact. The campaign achieved its immediate goal (getting people to read labels) but had limited effect on the ultimate goal (reducing sugar intake). This suggests barriers beyond awareness, such as limited healthy options, cost, or taste preferences.
Choice A is too absolute—the data shows label reading did correlate with some reduction, just not a dramatic one. We can't conclude it's ineffective based on this limited decrease. Choice B uses all-or-nothing thinking; a 3-gram reduction isn't failure, especially when the behavioral change was substantial. Choice C makes an unsupported causal claim—the data doesn't tell us that only the label-readers reduced their sugar intake, and we don't know if the 60% were responsible for the entire 3-gram drop.
Choice D correctly recognizes that campaigns can succeed at one level (behavior change) while having modest impact at another level (health outcomes). It acknowledges both the success and limitations without overstating either.
For GED questions about data interpretation, avoid extreme conclusions. Look for answer choices that acknowledge the complexity and nuance in real-world results rather than painting situations as complete successes or failures.
- Which statement best synthesizes the information about the college's online programs?
Answer: The college's online programs have grown significantly in popularity, but their students graduate at a lower rate than students who attend classes on campus.
When you encounter synthesis questions on the GED, you need to combine multiple pieces of information to draw a balanced conclusion that acknowledges all the key facts presented.
Looking at this passage, you have two main trends: online enrollment grew dramatically (from 15% to 35% over five years), but online students have lower graduation rates (45% vs. 60% for students with some in-person classes). A good synthesis captures both of these facts without overstating what they mean.
Choice A correctly synthesizes both pieces of information. It acknowledges the significant growth in popularity (more than doubling the percentage) while also noting the lower graduation rate. This balanced summary doesn't draw conclusions beyond what the data supports.
Choice B makes an extreme recommendation that isn't supported by the data. A 45% graduation rate, while lower than the alternative, doesn't necessarily mean the programs are "ineffective" or should be eliminated. Choice C commits a logical error by claiming the enrollment growth "proves" higher motivation, when the data actually shows online students graduate at lower rates. Choice D downplays the significance of a 15-percentage-point difference (45% vs. 60%), calling it "small" when it actually represents a 33% higher success rate for students with in-person classes.
For synthesis questions, avoid answer choices that ignore part of the data, make extreme recommendations, or draw unsupported conclusions. Look for the choice that fairly represents all the information without overinterpreting it.
- What can be inferred from the combination of the report's argument and its data?
Answer: The technology has proven effective, but its overall impact on national accident rates is limited by its low adoption rate.
When you encounter inference questions like this one, you need to combine multiple pieces of information logically rather than simply restating what's directly said. The passage gives you two key facts: AEB reduces rear-end collisions by 50% in equipped vehicles, and only 20% of cars have this technology.
The correct reasoning connects effectiveness with limited reach. Since AEB works well (50% reduction) but exists in few cars (20% adoption), its overall national impact must be constrained by the low adoption rate. This makes choice A correct—the technology proves effective but has limited overall impact due to low adoption.
Choice B commits a scope error by applying the 50% reduction to the entire country rather than just to the 20% of equipped vehicles. The data shows a 50% reduction only in cars that have AEB, not nationally.
Choice C makes an extreme logical leap. Just because 80% of cars lack AEB doesn't mean they cause all rear-end collisions—cars with AEB still have some accidents (just 50% fewer).
Choice D confuses correlation with causation. Low adoption rates could stem from many factors—cost, availability, consumer awareness—rather than perceived effectiveness. The passage doesn't provide information about driver beliefs.
For GED inference questions, remember to combine all given information logically without making extreme assumptions. Look for answers that account for limitations or qualifying factors mentioned in the passage, rather than those that overgeneralize from partial data.
- What conclusion is supported by combining the report's argument and the specific data for Elmwood?
Answer: Elmwood's low percentage of green space may be a contributing factor to the relatively low monthly park usage by its residents.
When you encounter questions asking you to draw conclusions from data, focus on what the evidence actually supports rather than what it might imply or prove definitively. You need to connect the pieces of information logically without overstating the relationship.
The report establishes that cities with over 20% green space have higher life satisfaction, while Elmwood has only 8% green space. Additionally, only 35% of Elmwood residents visit parks monthly. When you combine these facts, the most reasonable conclusion is that Elmwood's limited green space may contribute to the low park usage - there simply aren't enough parks for residents to visit regularly.
Choice A is wrong because the data doesn't tell us anything about residents' interest in outdoor activities generally - low park visitation could result from insufficient parks rather than lack of interest. Choice B makes an unsupported leap by assuming the 65% who don't visit parks monthly are "actively dissatisfied" - they might be satisfied with other amenities or simply have different preferences. Choice C overstates the evidence by claiming the report "proves" a causal relationship and predicts specific outcomes; correlation doesn't establish causation, and the data doesn't support precise predictions about doubling visitation.
Choice D correctly uses tentative language ("may be a contributing factor") and draws a reasonable connection between limited green space and low usage without claiming definitive causation.
Remember: on reasoning questions, look for conclusions that logically follow from the evidence without overstating relationships or making unsupported assumptions about causation.
