Component 1 Written Exam (9239): Practice Questions

These are original questions written for Marlbridge, in the style and at the standard of the examination. They are not reproduced past-paper questions — examination boards hold copyright in their own papers and their source materials. Use these alongside the official past papers available free from your board.

Related: Component 1 Written Exam revision notes

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Source

“A city that introduced a four-day working week saw productivity rise by 15% in the first year. Clearly, every country should adopt a four-day week to boost economic output. Business leaders who oppose this change simply don’t understand modern management.”

Section A

1. Deconstruct the argument in the source into its claim, reasons, evidence, assumptions and conclusion. [4]

2. Identify two reasoning flaws in the source and explain how each undermines the argument. [6]

Section B

3. Evaluate the strength of the evidence used in the source, referring to at least three evaluation criteria. [6]

4. Explain how a business owner in a small manufacturing firm and a government economist might hold different perspectives on this issue, with reference to their likely contexts. [6]

5. Using the source as a starting point, construct a brief, reasoned personal view on whether a four-day working week should be widely adopted. [8]

6. A second source claims: “Countries that invest more in education have higher GDP per capita. Therefore increasing education spending will raise national income.” Identify two reasoning flaws in this argument, distinct from the two you identified in Question 2, and explain each. [6]

7. Explain what is meant by describing global issues as “contested and interconnected,” using an energy policy as your example. [4]

8. Explain how you would evaluate a data-heavy report differently from an opinion piece on the same issue. [4]

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Answers

1. Claim: every country should adopt a four-day week [1]. Reasons/evidence: one city’s productivity rose 15% after adopting it [1]. Assumption: what worked in one city will generalise to every country and every kind of work [1]. Conclusion: a four-day week should be adopted everywhere [1].

2. Hasty generalisation — one city’s result is extrapolated to “every country,” ignoring differences in industry, economy and culture, so the evidence cannot support a claim that broad [3]. Ad hominem — dismissing opposing business leaders as not understanding modern management attacks the arguer rather than engaging with their actual objections, which weakens rather than strengthens the case [3]. (Other valid flaws, e.g. false cause between the policy and the productivity rise, credited equally.)

3. The evidence is a single city’s result, raising questions of sufficiency (one case cannot support a universal claim) [2]; methodology is unstated — no detail on how productivity was measured, over what period, or what else changed at the same time, undermining confidence in provenance and reliability [2]; and there is no corroboration from other cities or countries that have trialled similar policies, so the claim cannot yet be triangulated against other evidence [2].

4. A small manufacturing firm owner may prioritise fixed costs, machinery downtime, and tight profit margins, making a shorter week feel riskier — their perspective is shaped by the economic context of running a cost-sensitive business [3]. A government economist may weigh broader outcomes such as national productivity trends, employee wellbeing and labour-market effects, shaped by a policy-level, population-wide context rather than one firm’s balance sheet [3].

5. A strong response takes a clear position [1], directly engages with the source’s evidence while acknowledging its limitations (small sample, missing methodology) [3], weighs at least one perspective against another (e.g. small-firm cost concerns vs. wider productivity potential) [2], and reaches a conclusion that follows from the reasoning given, while acknowledging remaining uncertainty [2].

6. Reverse causation — the argument assumes spending causes higher GDP, but it is equally plausible that richer countries can afford to spend more on education, so the causal arrow may run the other way [3]. Confounding — factors such as governance, political stability and infrastructure could independently raise both education spending and GDP, so the correlation may not reflect a direct causal link between the two at all [3]. (Also accept: sufficiency — no data given on timescale or the size of any effect.)

7. Global issues are contested — reasonable people can disagree about the right response — and interconnected, meaning a decision in one area affects others [2]. An energy policy, for example, is simultaneously economic (cost and jobs), environmental (emissions), political (energy security) and ethical (fairness between generations) all at once, so addressing one dimension (e.g. cutting emissions) can create a new problem in another (e.g. raising costs for consumers) [2].

8. A data-heavy report is best interrogated for sample size, the baseline used, and whether a quoted percentage is hiding a small absolute number [2]. An opinion piece is best interrogated for the writer’s underlying assumptions and the strength of the reasons given, rather than simply agreeing or disagreeing with the conclusion stated [2].

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Where marks are usually lost

• Naming a fallacy without explaining specifically how it undermines the source’s inference.
• Evaluating evidence only on “it’s just one example” without naming the actual criteria (sufficiency, methodology, corroboration).
• Describing perspectives without grounding them in a specific context.
• A personal view that ignores the source entirely instead of using it as the starting point for reasoning.
• Confusing reverse causation with confounding — reverse causation means the causal arrow may run backwards; confounding means a third factor may be driving both variables.
• Analysing a contested global issue along only one dimension (e.g. only economic) rather than naming the interconnected trade-offs across others.
• Applying the same evaluation criteria to every source type, rather than adapting them to whether the source is data-heavy, opinion-based, or factual reporting.