- Which set of notes best uses the sources to answer the question "Did the pilot improve travel on Route 8?" while clearly separating facts from opinions?
Answer: Fact: Weekday trips averaged seven minutes less, and on-time arrivals rose. Opinion: Survey participants generally believed the route was more predictable, and the editorial supports permanence.
When a question asks you to separate facts from opinions, your job is to check every claim against its source: does the source actually measure it, or does someone simply believe it? Facts come from objective data (GPS records, percentages); opinions come from personal judgments or interpretations (surveys, editorials).
Answer A handles this cleanly. The GPS data directly measured a seven-minute average reduction in weekday trip time and an 11-point jump in on-time arrivals — those are verifiable facts from recorded data. The survey results and editorial claim are correctly labeled as opinions: survey responses reflect how riders felt, not what was objectively measured, and the editorial explicitly expresses a value judgment about making the lane permanent.
B is wrong on two counts. Calling improved travel a fact "for every Route 8 passenger throughout the week" overstates the data — the report excludes weekends and doesn't cover all riders. B also invents a causal link between rider support and the seven-minute reduction, which no source supports.
C reverses the fact/opinion labels entirely. The GPS data is objective evidence, not opinion, and calling "most riders experienced shorter commutes" a fact misrepresents the survey — only 27 percent of voluntary respondents said their commute got shorter, a self-selected group that can't represent all riders.
D is built almost entirely on fabrications. "Permanently transformed" comes from an editorial opinion, not a measured outcome, and the claim about weekend travel is invented — no source addresses weekend data at all.
Your strategy: before labeling anything a "fact," ask yourself, "Was this measured or recorded?" If it comes from someone's feeling, editorial stance, or speculation, it's an opinion regardless of how confidently it's stated.
- Which answer to "Did the expansion benefit vendors and residents?" is most appropriately supported by the combined sources?
Answer: The evidence is mixed: vendor participation and total sales rose, while surveyed residents reported both convenience and parking concerns; individual-level benefits for vendors or all residents are not established.
When a question asks you to evaluate a claim using multiple sources, your job is to weigh what each source actually establishes — and notice when evidence is limited, mixed, or overstated.
Here, the sources point in different directions. Administrative records show vendor count grew (18 to 27) and total sales rose 24%, which signals positive trends. The resident survey shows 59% of respondents found the extra day convenient, but 21% flagged parking difficulties. The vendor association calls the expansion a complete success for everyone. Taken together, the picture is genuinely mixed: some indicators improved, concerns exist, and broad claims about every vendor or all residents go beyond what the data can support. That makes D the strongest answer — it accurately reflects both the positive signals and the limitations without overstating either.
A is tempting but commits a critical error: it leaps from group-level trends to individual-level guarantees. A 24% rise in total sales doesn't mean every vendor earned more, and 59% convenience among survey respondents doesn't mean every resident benefited. Watch for words like "every" and "proves" — they almost always overreach.
B misreads the data twice. It assumes sales per day declined (possible, but not stated), and it exaggerates resident opposition — 21% reporting parking difficulty is not the same as residents opposing the expansion overall.
C is factually wrong. The sales and vendor figures come from administrative records (Source 1), not the vendor association's statement (Source 3).
Strategy tip: On source-evaluation questions, underline any absolute words ("every," "all," "proves"). If the evidence only supports a trend or majority, the correct answer will reflect that nuance rather than a sweeping conclusion.
- Which note most accurately answers "How much collected material was actually sent for processing in 2024?" and separates that answer from judgment?
Answer: Fact: 697 tons were accepted for processing. Opinion: The collection increase was impressive, and residents need clearer instructions.
When a question asks you to separate fact from opinion, your job is two-fold: identify what the sources actually report as data, and strip away any evaluative language (words like "impressive," "too much," or "should"). Keep those two categories clean.
Here, the question asks specifically how much collected material was sent for processing. Source 2 (the contractor's audit) gives you the clearest answer: of 820 tons collected, 123 tons were rejected as contamination, leaving 697 tons accepted and marketed to processing facilities. That 697 figure is a verifiable number from a third-party audit — it's a fact. The phrases "impressive success" (from the newsletter) and "residents need clearer instructions" (from the council member) are both judgments or recommendations, not data — they belong in the opinion column. Choice B handles this correctly: it states the accurate processing figure as fact and groups the evaluative language as opinion.
Choice A is wrong because it labels 820 tons as sent for processing — but 820 was the collected total, not the processed total. It also misidentifies the opinion portion entirely. Choice C introduces 760 tons, which was the 2023 collection figure, not a 2024 processing figure — it confuses years and categories. Choice D claims 123 tons went to processing facilities, but those 123 tons were the rejected material, the exact opposite of what was processed.
A reliable strategy: when sources give you a chain of numbers (collected → rejected → accepted), map each number to its specific meaning before answering. The question's wording is your guide — match the right number to the right step in the chain.
- Which summary best integrates the sources while preserving important limitations?
Answer: Saturday visits increased during the trial, and most survey respondents valued the later closing; however, the help desk and incomplete survey response limit causal claims.
When a question asks you to integrate multiple sources, your job is to combine what each source actually shows — without overstating the evidence or dismissing it entirely. Pay close attention to which sources support strong conclusions and which ones introduce uncertainty.
The library records confirm a real increase in Saturday visits (430 vs. 350 per month), so something changed during the trial. The exit survey shows that 71 out of 96 respondents valued the later closing — that's meaningful, but only 96 of 180 invited visitors responded, meaning more than half didn't participate. Non-respondents might feel differently. On top of that, the new help desk also launched on Saturdays during the trial, so you can't be certain whether the extended hours, the help desk, or some combination drove the increase. Answer B accurately reflects all of this: visits rose, most respondents valued the later hours, but the incomplete survey response rate and the help desk as a competing explanation both limit how confidently you can assign cause.
Answer A echoes the Friends of the Library's overconfident language ("unquestionably caused"), ignoring the help desk and survey limitations entirely. Answer C swings too far the other way — it dismisses any effect of the trial, which contradicts the real visit increase the records document; no source claims the help desk was solely responsible. Answer D is factually wrong on two counts: not every visitor was surveyed, and attendance did not remain "nearly unchanged" — it rose by 80 visits per month.
A useful strategy: when sources conflict or overlap, look for the answer that acknowledges both the evidence and its limits — that balance is almost always what "integrating sources" means on this exam.
- Which research note most accurately distinguishes observed evidence from estimates and personal judgment?
Answer: Observed fact: Weather-adjusted gas use fell 11 percent. Estimate: Use might fall 18 percent under a stated condition. Opinion: The seals were a waste.
When a question asks you to sort information by type — fact, estimate, or opinion — your job is to trace each claim back to its source and ask: Was this measured? Predicted? Or felt? Observed facts come from real measurements or records. Estimates are projections based on conditions or assumptions. Opinions are personal judgments that can't be verified objectively.
Answer A handles all three categories correctly. The 11 percent gas reduction comes from actual building invoices adjusted for weather — that's a recorded, measurable outcome, making it an observed fact. The 18 percent figure came from the contractor's proposal, written before installation and only valid "if windows were the building's main source of heat loss" — a conditional prediction, which is exactly what an estimate is. The tenant's claim that the seals were "a waste of money" is a value judgment based on how the hallways feel, not on data — that's an opinion.
Answer B misassigns everything. The 18 percent figure was never observed; it was a pre-installation estimate. Cold hallways are what the tenant experienced, not a prediction. And occupancy data came from building records, making it a fact, not an opinion.
Answer C makes the most serious error: it treats the tenant's feelings as a proven fact ("cold hallways prevented any energy savings") and demotes the actual invoice data to an estimate. Feelings don't override measurements.
Answer D falsely upgrades the 11 percent drop into a cause-and-effect proven fact — the invoices show correlation, not confirmed causation. Occupancy and weather adjustments are documented facts, not estimates or opinions.
Your tip: when sorting evidence types, ask yourself, "Was this recorded, predicted, or felt?" That question will cut through most of these traps quickly.
- Which recommendation is best supported by all three sources?
Answer: Conduct a limited evening-course trial while tracking enrollment, since some applicants reported scheduling conflicts but broader demand remains uncertain.
When a question asks which recommendation is "best supported by all three sources," your job is to find the option that accurately reflects every source — not just one or two. Think of it as a logic check: if any source contradicts or fails to support a choice, that choice fails.
Here, the three sources paint a consistent but cautious picture. Source 1 shows some demand — 25 out of 41 survey respondents cited scheduling conflicts — but that's a partial sample of a partial group, leaving real uncertainty about total demand. Source 2 shows possible employer interest (9 of 14 responding employers said they'd consider sharing information), but only 14 of 60 employers even responded, so actual commitment is unknown. Source 3 explicitly calls for testing the idea while acknowledging the evidence is insufficient to guarantee a permanent program. Together, all three sources justify a cautious trial with continued monitoring — which is exactly what C recommends.
A fails because neither applicants nor employers have "committed" to anything. Declining to enroll doesn't equal commitment, and "would consider sharing information" is far weaker than a promise to participate. B distorts Source 2 entirely — the survey never mentioned tuition reimbursement, and employers not responding is not the same as refusing to pay tuition. D misreads Source 1; the questionnaire listed scheduling conflicts as one reason among others, and only 25 of 41 partial respondents selected it — far from proof it was the only reason.
A useful strategy: watch for answer choices that use absolute language like "only," "all," or "committed." Real-world data almost never supports such strong conclusions, and those words are usually traps.
- Which conclusion best uses the sources and accounts for the difference in hours worked?
Answer: The injury rate fell from 7 to 5 injuries per 100,000 hours, but the sources do not prove that training alone caused the decline.
When a question gives you raw counts alongside different time periods or workloads, your first move should be to convert to a rate so you're comparing apples to apples. Raw numbers alone can mislead.
The standard workplace injury rate is calculated per 100,000 hours worked:
Rate=hours workedinjuries×100,000
Before training: 200,00014×100,000=7 per 100,000 hours
After training: 160,0008×100,000=5 per 100,000 hours
So the rate did fall — from 7 to 5. But Source 3 flags that total hours and production volume both dropped, meaning other factors changed alongside the training. You cannot isolate training as the sole cause. That's exactly what answer A captures: a real rate improvement that cannot be definitively attributed to training alone. A is correct.
B is wrong on two counts: the rates it states (14 and 8) are the raw injury counts, not rates per 100,000 hours. It also claims employee support proved effectiveness — a logical leap no source supports.
C is wrong because it claims the rate was unchanged. The rate clearly dropped from 7 to 5; this answer confuses "both numbers fell" with "the ratio stayed the same."
D reverses the direction entirely, stating the rate increased from 5 to 7. That gets the before/after values backwards.
Strategy tip: Whenever you see data with different totals across time periods, always ask "rate or raw count?" Raw counts without context are almost never the full story on this exam. - Which note most accurately combines the sources without presenting an opinion or an uncertain cause as an established fact?
Answer: Unfilled shifts were less common during the test, although the records do not isolate the portal from seasonal changes; employees expressed mixed opinions about convenience.
When combining multiple sources, your job is to report only what the evidence actually shows — not to assign causes, declare winners, or smooth over contradictions. Ask yourself: Does this claim go beyond what the data can prove?
Answer B earns its accuracy by honoring the limits of each source. It acknowledges the staffing improvement (unfilled shifts dropped from 12% to 5%) without crediting the portal as the definite cause — because the holiday season was also underway, making it impossible to isolate which factor drove the change. It also captures the genuine split in employee opinion rather than flattening it into a single verdict. Nothing in B is stated with more confidence than the sources allow.
Answer A commits the clearest error by calling the drop in unfilled shifts proof that employee choice eliminates scheduling problems. That conclusion overstates the data significantly — no source proves causation, and the manager's claim in Source 3 is an opinion, not an established fact. "Even during unusually busy periods" also smuggles in a favorable interpretation.
Answer C scrambles the sources badly. It claims employees preferred the portal because staffing improved — reversing the logical relationship — and incorrectly attributes the "shifts disappearing quickly" complaint to the manager rather than to the employees who actually said it.
Answer D invents a causal claim that no source makes: that the holiday season caused employees to use the portal more often. It also overstates employee opinion by saying comments "established" the system was convenient overall, when reactions were mixed.
Strategy tip: When a question asks you to combine sources neutrally, flag any word that implies certainty — proved, caused, established, clearly — as a red-flag signal that an answer is overreaching.
- Which labeling of notes correctly distinguishes reported facts from opinions or predictions?
Answer: Fact: Sixty-one participants had first- and final-week step records. Opinion/Prediction: Lunchtime walks were more enjoyable, and future claims may decline with continued participation.
When reading informational sources, your job is to separate what was directly observed or measured (facts) from what someone believes, feels, or projects into the future (opinions and predictions). Ask yourself: "Could this be verified with data, or does it depend on someone's judgment?"
Source 1 gives you measurable data: 61 out of 86 enrolled employees had complete records, and among those 61, the median daily step count rose from 5,900 to 7,100. These are documented, verifiable numbers. Source 2 is a personal interview — the participant uses phrases like "I am certain" and "more enjoyable," which signal subjective belief, not measured outcomes. Source 3 explicitly uses the word "predicts" and confirms "no claims data have yet been analyzed," making it clearly a forward-looking prediction.
Answer C correctly labels the 61-participant data point as a fact (it comes directly from the program log) and correctly groups the enjoyment claim and the insurance prediction together as opinion/prediction — because one is a personal feeling and the other is an unverified forecast.
Answer A gets things backwards, misidentifying the measurable median increase as an opinion and falsely claiming all 86 participants increased their steps — the log only tracked 61. Answer B treats a subjective interview quote about "everyone's health" as a fact, which it is not; personal certainty is not evidence. Answer D invents a fact that doesn't exist in any source — insurance claims have not already declined — and then mislabels an unsupported inference as an opinion.
A useful habit: whenever a source includes words like predict, certain, enjoyable, or could, flag that statement as opinion or prediction, not fact.
- Which conclusion best answers whether the ordinance increased the number of living trees in the regulated area?
Answer: The available sources do not establish the result: plantings exceeded removals, but survival was unreported and countywide canopy data do not isolate the regulated area.
When evaluating whether a policy achieved its goal, you must ask: do the sources actually measure the outcome, or only measure inputs and proxies? Here, the outcome you need is the net change in living trees inside the regulated area specifically.
The permit records (Source 2) confirm that 420 trees were planted and 310 were removed — a net gain of 110 plantings on paper. But the records explicitly do not track survival. A planted tree is not the same as a living tree. Source 1 shows a 1-point countywide canopy increase, but it covers land outside the ordinance area and spans a period with severe storms, so it cannot isolate what happened inside the regulated zone. Because survival data are missing and the canopy data are too broad, no source closes the logical gap between "trees were planted" and "trees are alive and thriving." That gap makes C the only defensible conclusion.
Answer A is tempting but overreaches — it treats unconfirmed plantings and an unrelated countywide metric as proof of success. Both pieces of evidence have documented limitations that A ignores. Answer B makes the opposite error: it treats a small canopy increase as proof of mass failure, which is a logical leap. The canopy data cannot prove that; many explanations exist for a modest countywide number. Answer D leans entirely on the business coalition's opinion (Source 3), which is advocacy, not evidence — and blaming storms doesn't answer whether the ordinance worked.
The strategy to remember: gap-check every conclusion. Ask yourself whether the evidence actually measures what the conclusion claims, or whether a critical piece — here, survival data — is simply missing.
