Command of Evidence (Quantitative) Flashcards

A researcher studying urban heat islands collected temperature data across five city zones over two decades. The table below shows mean summer temperatures (°C) in 2003 and 2023: | Zone | 2003 | 2023 | |------|------|------| | Industrial | 34.1 | 37.8 | | Commercial | 32.6 | 35.9 | | Residential-Dense | 31.4 | 33.2 | | Residential-Sparse | 29.8 | 30.6 | | Green Space | 27.3 | 27.9 | The researcher claims that 'zones with higher baseline temperatures in 2003 experienced disproportionately greater absolute warming by 2023.' Which choice best evaluates this claim using the data?

Answer: The claim is supported: Industrial gained 3.7°C and Commercial gained 3.3°C, while Green Space gained only 0.6°C, showing a clear positive correlation between baseline temperature and warming magnitude.

Choice A correctly identifies the core pattern: the two highest-baseline zones (Industrial at 34.1°C and Commercial at 32.6°C) also show the two largest absolute gains (3.7°C and 3.3°C), while the lowest-baseline zone (Green Space at 27.3°C) shows the smallest gain (0.6°C). This directly supports the researcher's claim with specific quantitative evidence. Choice B misapplies the logic by comparing within the lower-baseline group rather than across the full range. Choice C is too vague—'all zones increased' does not demonstrate disproportionality. Choice D incorrectly claims the data is insufficient when the table provides exactly the values needed.

A student presents the following graph data about streaming platform subscribers (in millions) across four quarters of 2025: | Platform | Q1 | Q2 | Q3 | Q4 | |----------|----|----|----|----|| | StreamA | 145 | 152 | 161 | 158 | | StreamB | 89 | 94 | 99 | 107 | | StreamC | 203 | 198 | 191 | 187 | | StreamD | 41 | 55 | 68 | 84 | The student writes: 'By year's end, StreamD had achieved the most remarkable growth trajectory of all platforms.' A classmate argues that StreamC's dominant subscriber base makes it the most remarkable story. Which of the following, if added to the passage, would most effectively use the quantitative data to resolve this disagreement in favor of the student's original claim?

Answer: StreamD grew by approximately 105% from Q1 to Q4 (from 41 to 84 million), while StreamA grew by only 9%, StreamB by 20%, and StreamC actually declined by 8%, making StreamD's trajectory uniquely expansionary among all four platforms.

Choice A is the strongest resolution because it directly addresses the disagreement using percentage growth—the metric most relevant to 'growth trajectory.' It quantifies StreamD's ~105% growth rate against all other platforms, including StreamC's actual decline, providing a complete comparative picture. Choice B acknowledges StreamD's gains but concedes ground to the classmate by highlighting StreamC's size advantage, which weakens rather than resolves the dispute. Choice C uses qualitative inference ('poor content strategy') rather than the quantitative data alone. Choice D introduces a forward-looking projection that goes beyond what the data supports and doesn't address the original trajectory comparison directly.

The following table shows data from a clinical trial comparing two blood pressure medications over 12 weeks: | Metric | Drug X | Drug Y | Placebo | |--------|--------|--------|----------| | Mean systolic BP reduction (mmHg) | 14.2 | 11.8 | 3.1 | | % patients achieving target BP | 67% | 54% | 18% | | Mean side effect severity (1–10 scale) | 6.4 | 3.2 | 1.1 | | Trial dropout rate | 23% | 11% | 6% | A pharmaceutical company's advertisement states: 'Drug X outperforms Drug Y across all clinically meaningful outcomes.' Which choice most precisely identifies the flaw in this claim using the table?

Answer: The claim is inaccurate because Drug X does not outperform Drug Y on all clinically meaningful outcomes: Drug Y produces lower side effect severity (3.2 vs. 6.4) and a lower dropout rate (11% vs. 23%), both of which are clinically significant metrics that favor Drug Y.

Choice A directly uses specific quantitative data from the table to refute the claim. The advertisement says Drug X outperforms on 'all clinically meaningful outcomes,' but two table metrics—side effect severity (Drug Y: 3.2 vs. Drug X: 6.4, nearly half as severe) and dropout rate (Drug Y: 11% vs. Drug X: 23%, less than half)—clearly favor Drug Y. These are observable in the data and are clinically meaningful. Choice B makes a methodological point about advertisement design but doesn't use the data to directly test the claim as stated. Choice C introduces an outside clinical threshold judgment not derivable from the table. Choice D raises a study-duration concern that the table cannot address, making it a speculation rather than a data-based evaluation.

A sociologist studying public library usage collected the following data across three demographic groups over five years: | Year | Group A (visits) | Group B (visits) | Group C (visits) | |------|-----------------|-----------------|------------------| | 2019 | 12,400 | 8,700 | 5,200 | | 2020 | 3,100 | 2,900 | 4,800 | | 2021 | 6,800 | 5,400 | 5,600 | | 2022 | 10,200 | 7,100 | 5,900 | | 2023 | 13,600 | 9,300 | 6,400 | The sociologist concludes: 'The pandemic's disruption to library usage was not uniform across demographic groups, and Group C demonstrates a fundamentally different relationship with library services.' Which choice best supports this conclusion with precise evidence from the table?

Answer: While Groups A and B saw dramatic declines of approximately 75% and 67% respectively from 2019 to 2020, Group C declined by only 8% in the same period, and unlike the other groups, Group C's usage never returned to its pre-pandemic trajectory—it instead settled into a new, modestly higher baseline, suggesting a structural rather than temporary shift.

Choice A is correct because it uses precise percentage calculations to establish two distinct quantitative claims: (1) Groups A and B fell dramatically (~75% and ~67%) while Group C barely declined (~8%), demonstrating non-uniform disruption; and (2) Group C's post-pandemic usage pattern (5,600 → 5,900 → 6,400) represents a gradual upward trend from a new baseline rather than a recovery arc, which is qualitatively different from Groups A and B's V-shaped recovery. This fully supports 'fundamentally different relationship.' Choice B correctly notes resilience but attributes it to group size rather than structural behavior difference. Choice C inverts the math: Group C grew from 5,200 to 6,400 (about 23%), which is actually higher growth than Groups A and B, making 'weaker recovery' incorrect. Choice D is factually inaccurate—Group C declined from 5,200 to 4,800 in 2020, it did not increase.

Two researchers debate the findings from the following experiment measuring plant growth (cm) under different light conditions over 6 weeks: | Light Condition | Week 2 | Week 4 | Week 6 | |----------------|--------|--------|--------| | Full Sun (8h) | 4.2 | 9.8 | 16.1 | | Partial Sun (4h) | 3.9 | 8.6 | 13.4 | | Grow Light (8h) | 4.5 | 9.2 | 14.8 | | Low Light (2h) | 1.8 | 3.3 | 4.9 | Researcher 1 claims: 'Natural sunlight produces superior plant growth compared to artificial grow lights at equivalent durations.' Researcher 2 responds: 'The grow light's early advantage actually suggests artificial light is more efficient in initial growth stages.' Which choice most accurately characterizes what the data does and does not support?

Answer: The data partially supports Researcher 1's claim—Full Sun leads Grow Light by 1.3 cm at Week 6 (16.1 vs. 14.8)—but also partially supports Researcher 2's claim, since Grow Light leads Full Sun at Week 2 (4.5 vs. 4.2); however, neither claim about 'superiority' or 'efficiency' can be fully established from growth measurements alone, as the data does not account for energy consumption or growth rate per unit input.

Choice A is correct because it provides the most precise and honest reading of the data. It acknowledges what the numbers show—Full Sun's 1.3 cm advantage at Week 6 and Grow Light's 0.3 cm advantage at Week 2—while correctly noting that 'efficiency' as a concept requires information not in the table (e.g., energy cost per centimeter of growth). This is a sophisticated evaluation that avoids overreaching beyond the data. Choice B ignores Grow Light's Week 2 lead and treats a single-metric endpoint as total proof of superiority. Choice C commits a logical error: a higher number at one time point does not establish photosynthetic efficiency without controlling for light quality and quantity. Choice D incorrectly uses a mid-study data point to dismiss both researchers when in fact Week 6 data does support a difference.

A consumer report contains the following table on electric vehicle (EV) battery range degradation: | Model | Range at Purchase (miles) | Range at 50k miles | Range at 100k miles | Range at 150k miles | |-------|--------------------------|-------------------|--------------------|-----------------------| | Alpha EV | 310 | 294 | 271 | 248 | | Beta EV | 285 | 278 | 268 | 255 | | Gamma EV | 340 | 309 | 272 | 231 | | Delta EV | 265 | 261 | 254 | 246 | An automotive journalist writes: 'For drivers who expect to keep their vehicle well beyond 100,000 miles, the data presents a counterintuitive recommendation.' Which vehicle and quantitative reasoning most logically completes this claim?

Answer: The counterintuitive recommendation is Delta EV: despite having the lowest initial range (265 miles), Delta loses only 19 miles by 150k miles (a 7.2% degradation), whereas Gamma EV—the highest initial range at 340 miles—loses 109 miles (32.1%), meaning a high-mileage Delta driver retains more functional range than a high-mileage Gamma driver whose battery has severely degraded.

Choice A correctly identifies the 'counterintuitive' element: most buyers would choose Gamma EV (highest range at purchase: 340 miles), but by 150k miles, Gamma has degraded by 109 miles (32.1%), leaving 231 miles. Meanwhile, Delta—the least appealing by initial range at 265 miles—loses only 19 miles (7.2%), leaving 246 miles. A high-mileage Delta driver ends up with more remaining range than a high-mileage Gamma driver, which is genuinely counterintuitive. Choice B is incorrect because 255 (Beta) > 246 (Delta) at 150k miles, which means Beta is actually the higher-retaining option—but the claim says 'counterintuitive,' and Beta's higher initial range isn't surprising; the true surprise is Delta (lowest start, highest retention rate). Choice C describes Alpha accurately but Alpha's result isn't counterintuitive—it started with a high range and maintained it somewhat. Choice D inverts the logic: if Gamma's 231 < Delta's 246 at 150k, Gamma is NOT the better long-term choice despite the highest initial range.