- Based on the three data sources, which statement about cross-source consistency is most accurate?
Answer: Source C's percentage breakdowns contradict Source A's explicit account counts, indicating classification errors in either individual or business account categories.
Source A explicitly states 18,960 individual accounts out of 34,720 total, which equals 54.6%. Source C states individual accounts represent 54.8% of 34,650 accounts, which would be 18,988 individual accounts. The percentages are close but the absolute numbers don't align properly. More importantly, Source C says individual accounts hold 43.2% of $851,200,000 = $367,718,400 in assets. If Source A's individual accounts ($847,300,000 total) had the same proportion, they'd hold about 43.2% as well, but Source A shows a different total asset base. This suggests classification inconsistencies between individual/business categories. Choice A misses the classification issue. Choice B incorrectly suggests transaction volume is inconsistent (2.8M transactions for 34K accounts is reasonable). Choice C incorrectly claims consistency exists.
- Segment report: profit $10M. Corporate model lists contribution $12M and allocated overhead $2M. Consistent?
Answer: Cannot tell; costs may exist
Contribution of $12M and allocated overhead of $2M give $10M only if those are the only costs. Other unlisted costs could exist, so segment profit of $10M may or may not be consistent. The tempting error is assuming 12 - 2 = 10 proves it; that ignores possible additional costs.
- Year-end inventory log: 120 units. June delivery invoice: 120 units. Support same balance?
Answer: Cannot tell; sales may occur
The two numbers being 120 only shows the invoice and log match numerically. The invoice is from June while the log is year-end, so months of sales or purchases could happen in between; a June delivery doesn't prove the year-end balance is still 120. The tempting error is choosing 'Yes; the figures match' because equal numbers look consistent, but the different dates mean you cannot conclude.
- Based on the information provided, which analysis of cross-source consistency is most accurate?
Answer: Source Y's dosage data is inconsistent with the patient enrollment from Source X, indicating systematic under-dosing or incomplete logging.
To check consistency: If 480 patients were enrolled and received daily doses for 12 weeks (84 days), the expected total doses would be 480 × 84 = 40,320 doses. However, Source Y reports 43,200 doses administered, which exceeds this maximum possible amount, indicating a significant inconsistency. Choice B is wrong because 456 ≠ 480 (24 patients didn't complete). Choice C is wrong due to the dosage inconsistency identified above. Choice D is wrong because a 12-week treatment period within a January-June assessment timeline is reasonable for clinical trials.
- Which analysis of cross-source consistency is most accurate based on this healthcare data?
Answer: Source Green's billing records align closely with Source Blue's visit records, but show concerning discrepancies in specialty clinic encounter counts.
Let's verify the data: Source Blue shows 17,850 actual visits vs Source Green's 17,735 billed encounters - only 115 difference (good alignment). However, examining specialty clinics specifically: Source Blue indicates specialty no-show rate of 18.9%, so actual visits = 5,340 × (1 - 0.189) = 4,331 visits. But Source Green shows 5,630 specialty encounters billed, which exceeds even the original 5,340 scheduled appointments. This is impossible and indicates a significant discrepancy. Choice A is wrong - the overall no-show calculation is consistent: (4,680×0.021 + 8,920×0.157 + 5,340×0.189) ÷ 18,940 = 12.2%, close to reported 12.3%. Choice C misses the specialty clinic discrepancy. Choice D incorrectly characterizes this as systematic billing problems rather than the specific specialty clinic inconsistency.
- When evaluating these three data sources for consistency, what is the most significant discrepancy that requires investigation?
Answer: Source B's active customer count is lower than Source A's purchasing customers, which violates the logical relationship between these metrics.
The critical inconsistency is that Source B shows 14,965 active customers while Source A shows 15,420 customers made purchases. This is impossible since customers who made purchases must be active customers. The number of purchasing customers cannot exceed the number of active customers. Choice A is incorrect: 15,420 × $67.30 = $1,037,646 (matches exactly). Choice B describes a normal relationship - more customers received emails than purchased. Choice D is incorrect: 3,378 post-email purchases × $67.30 average = $227,339, which is easily supported within the total revenue of $1,037,646.
- Based on the three data sources, which statement about cross-source consistency is most accurate?
Answer: Source 2 productivity data is inconsistent with Source 1 staffing levels, indicating either underreporting of work or overestimation of time requirements.
To evaluate consistency, we need to check if the reported work output aligns with available capacity. Marketing: 180 projects × 0.8 person-months = 144 person-months needed, but with 45 employees over 3 months = 135 person-months available. Sales: 228 deals × 0.5 = 114 person-months needed vs. 38 × 3 = 114 available (consistent). Operations: 312 orders × 0.4 = 124.8 person-months needed vs. 52 × 3 = 156 available (consistent). Marketing shows a capacity shortfall, indicating inconsistency in Source 2. Choice A is wrong because financial data is proportional to headcount as stated. Choice B is wrong due to the Marketing inconsistency. Choice D is wrong because the allocation method is clearly described and doesn't contradict productivity ratios.
- HR: 20 employees left. Payroll: 5 retired, 10 resigned, 6 dismissed. Consistent?
Answer: No; components total 21
Add the components: 5 retired plus 10 resigned plus 6 dismissed equals 21, not 20. So the categories don't match the stated total of employees who left. The tempting wrong answer is the one that says 5+10+6 equals 20, but it actually sums to 21.
- What is the most significant consistency issue when comparing these three data sources?
Answer: Source 2 delivery counts don't align with Source 1 distribution percentages, suggesting incomplete fleet tracking or order classification errors.
Source 1 indicates order distribution of 35% same-city (3,129 orders), 45% regional (4,023 orders), and 20% national (1,788 orders) based on 8,940 total orders. Source 2 shows 2,150 + 3,420 + 1,680 = 7,250 total deliveries, which is significantly less than 8,940 orders processed. Additionally, the proportions don't match: Source 2 shows 2,150/7,250 = 29.7% same-city vs. 35% expected. This suggests incomplete tracking or classification problems. Choice B incorrectly characterizes the relationship between promises and actual times. Choice C is wrong because the weighted average of actual times roughly matches promises. Choice D misses the more fundamental counting discrepancy.
- Report A: 60% of 1,000 patients improved. Report B: 75% of the 800 who returned improved. Assess consistency.
Answer: No conflict; counts are 600
Both reports give the same count: 60% of 1,000 is 600, and 75% of 800 is also 600. The tempting mistake is to compare the rates directly, but the percentages apply to different base groups. Since both report 600 improved, there is no conflict.
- Survey: 90% of buyers repurchased. Finance: repeat orders are 10% of sales. Are sources consistent?
Answer: No conflict; metrics differ
These numbers don't measure the same thing: 90% of customers made another purchase, while repeat orders are only 10% of total sales volume. A large share of buyers can account for a small share of orders if most customers order once or repeat buyers order less often. The tempting mistake is calling it a conflict because 90 and 10 seem far apart, but the metrics differ, so they can both be true.
