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GMAT · Evaluating arguments · TOPIC LESSON

Strengthening and weakening arguments

Judge how new evidence changes the support for one exact conclusion.

GMAT45 min study guide9 indexed sections

BY THE END, YOU CAN

  • Explain and apply identify the exact claim
  • Explain and apply strengthening links
  • Explain and apply weakening assumptions
  • Explain and apply reverse causation
  • Explain and apply control and comparison groups
  • Explain and apply denominator changes
  • Explain and apply selection bias
  • Explain and apply prediction assumptions
  • Explain and apply distinguish relevant evidence from background

BEFORE YOU START

  • Read an argument or passage without assuming that every speaker agrees.
  • Distinguish a claim from the evidence offered for it; interpret simple counts and percentages.

Original educational lesson. Work through the examples before revealing the practice answers. This original lesson supports current GMAT study through an editorial skill map. It does not reproduce official questions, predict exam content or guarantee a score.

LEARN THROUGH ORIGINAL PRACTICE

Original GMAT-style worked examples and practice

All passages, examples and exercises here are original. No official GMAC questions, choices or explanations are reproduced. This independent preparation resource is not affiliated with or endorsed by GMAC. GMAT is a trademark of the Graduate Management Admission Council.

01 / UNDERSTAND & APPLY

Find the exact claim before changing its support

Strengthen and weaken questions ask about the relation between evidence and a conclusion. Accept a proposed option as true for the question and ask how it changes the conclusion’s support. A strengthener need not prove the conclusion; a weakener need not disprove it.

Separate a claim about causation from one about prediction, profitability or policy. Evidence that a product works can support effectiveness while leaving profitability unresolved. Write a one-sentence target before evaluating options.

ORIGINAL WORKED EXAMPLE

Choose the target

A delivery company’s pilot routing software reduced kilometers per parcel. The director concludes that buying it will lower annual operating costs. What must a relevant option address?

  1. The conclusion concerns total annual operating costs, not merely route length.
  2. License fees, implementation expense, fuel savings and usable performance all bear on the conclusion.
  3. A fact about the logo or developer’s age does not connect to that cost claim unless an additional link is supplied.

Answer Evidence affecting net annual costs under adoption.

03 / UNDERSTAND & APPLY

Alternative causes and reverse causation

If people with a habit have an outcome, three stories are immediately possible: the habit causes the outcome, the outcome encourages the habit, or a third factor produces both. Relevant evidence distinguishes these stories. A mere correlation stated again is usually weak additional support.

Temporal order can matter: if an outcome improvement occurred before the supposed cause began, that weakens attribution to the cause. Timing alone does not establish which alternative explanation is true.

ORIGINAL WORKED EXAMPLE

Reverse causation

Employees who attended optional planning workshops completed projects sooner. Management credits the workshops. New records show that employees were invited only after they had already improved their completion times. What happens to the argument?

  1. Improvement precedes workshop attendance in the described selection process.
  2. The association may reflect selection of already-improving employees rather than workshop effects.
  3. This weakens the causal attribution without proving workshops could never help anyone.

Answer The argument is weakened by selection based on a preexisting improvement.

04 / UNDERSTAND & APPLY

Control and comparison groups

A control group helps estimate what would have happened without the intervention. Its value depends on relevant comparability, consistent measurement and absence of differential changes. A before–after improvement can be due to a general trend affecting everyone.

Random assignment can strengthen causal inference by distributing preexisting differences, but it is not a magic label. High differential dropout or different outcome measurement can reintroduce bias. In a question, focus on the actual detail rather than demanding perfection from every study.

ORIGINAL WORKED EXAMPLE

A comparison that matters

A factory changes shift reminders; late arrivals fall. Which strengthens attribution to reminders? A: Other similar factories without changed reminders show no fall, and no other relevant policy changed at the factory. B: The reminder font is popular. C: Attendance records have always existed. D: The manager likes punctuality. E: The factory opened long ago.

  1. A gives a comparison against a general trend and reduces a competing policy explanation.
  2. It does not guarantee causation, but it makes the proposed explanation better supported.
  3. B through E do not explain why late arrivals changed at this time.

Answer A

05 / UNDERSTAND & APPLY

Denominator changes can reverse an apparent trend

When a conclusion concerns a count but evidence concerns a rate, or vice versa, look for denominator changes. If customer complaints drop from 4% of 1,000 orders to 3% of 2,000 orders, complaints rise from 40 to 60 even though the rate falls.

Choose evidence that changes the relevant denominator, not merely any numerical fact. A rise in advertising spending may be irrelevant to a complaint-count inference unless it connects to order volume or measurement.

ORIGINAL WORKED EXAMPLE

A rate is not a count

A retailer says complaints fell because the complaint rate fell from 4% to 3%. New data show orders doubled from 1,000 to 2,000. Evaluate.

  1. Old complaints: 0.04×1,000=40. New complaints: 0.03×2,000=60.
  2. The rate premise remains true, but the count conclusion is false under the supplied figures.

Answer The new evidence undermines the count conclusion: complaints rose by 20.

06 / UNDERSTAND & APPLY

Selection bias and future conditions

People who choose a service may differ from nonusers before using it. A tutoring program’s volunteers may be more motivated; a premium repair plan’s users may own newer equipment. Evidence of preexisting differences weakens a simple causal attribution.

Predictions also rely on conditions remaining relevant: demand, capacity, prices, regulations or participant response. A forecast based on unused capacity may fail when the system is already saturated. Identify the mechanism before deciding whether a change matters.

ORIGINAL WORKED EXAMPLE

A prediction under changed demand

Last year a café sold every extra lunch it made during a nearby office renovation. It predicts the same result this year. The office cafeteria has now reopened. Does this matter?

  1. The forecast relies on comparable demand for outside lunches.
  2. A reopened substitute can reduce demand even if last year’s sales record is accurate.
  3. It does not prove every extra lunch will go unsold; it reduces support for the repeated-sales prediction.

Answer Yes; the changed alternative weakens the demand-transfer assumption.

07 / UNDERSTAND & APPLY

Direction, magnitude and relevance

For each option ask: if true, does it make this conclusion more likely, less likely, or neither? Then ask whether the effect is direct enough to distinguish it from other options. A relevant fact can be too weak to be the best choice when another targets the central gap.

Do not smuggle in extra premises. “The manager is experienced” does not automatically establish that a particular forecast is accurate. Similarly, a fact that criticizes an unrelated practice does not weaken the stated claim.

08 / UNDERSTAND & APPLY

Practice: identify the direction of evidence

Each item states the target conclusion. Treat its new evidence as true.

ORIGINAL PRACTICE · 1

1. A gym credits a new app for improved attendance. Users were selected because their attendance had already risen. Strengthen or weaken?

Reveal answer & reasoning

Weaken.

Selection on prior improvement supplies an alternative explanation for the association. The data do not isolate an app effect.

ORIGINAL PRACTICE · 2

2. A ferry operator predicts profit from a new route. Which most weakens? A: Passengers like the boat color. B: Route revenue is projected below unavoidable route-specific operating costs. C: Tickets are sold online. D: The captain has a uniform. E: The route has a name.

Reveal answer & reasoning

B

Profit requires revenue to exceed relevant costs. B directly undermines it; A, C, D and E do not establish the missing financial relation.

ORIGINAL PRACTICE · 3

3. A training program claims to reduce errors. Comparable randomly assigned groups were tested identically; only the trained group showed a substantial decline, with no differential dropout. What is the effect?

Reveal answer & reasoning

Strengthens the causal claim.

Relevant comparability, common measurement and retained groups reduce several alternatives. The evidence supports causation without making all possible uncertainty disappear.

ORIGINAL PRACTICE · 4

4. A firm reports a fall in return rate from 8% to 6%, claiming fewer returns. Sales rise from 500 to 1,000 units. Compute and evaluate.

Reveal answer & reasoning

Returns rise from 40 to 60, weakening the count claim.

8%×500=40; 6%×1,000=60. The lower rate does not imply a lower count.

ORIGINAL PRACTICE · 5

5. A bus-frequency increase is credited for shorter waits. A neighboring unchanged route had an equal wait reduction after traffic restrictions were lifted. What does that do?

Reveal answer & reasoning

Weakens the attribution by supporting a broader traffic explanation.

The comparison suggests waits could have fallen without the frequency increase. It does not prove frequency had zero effect.

ORIGINAL PRACTICE · 6

6. A shop predicts holiday sales at least as high as last year’s; this year its only nearby competitor has closed. What is the likely direction of this evidence, all else equal?

Reveal answer & reasoning

It strengthens the prediction of sales at least as high as last year’s, but does not guarantee it.

With other relevant conditions unchanged, removal of a nearby substitute supports greater demand at the shop and therefore supports the stated lower-bound sales prediction. Capacity, customer behavior and other factors can still affect realized sales. This does not specifically establish that sales will be exactly equal to last year’s.

09 / UNDERSTAND & APPLY

Practice: background versus a real test

Identify the exact conclusion and avoid unearned links.

ORIGINAL PRACTICE · 1

7. A city claims a refill station will reduce disposable-bottle use. Which most strengthens? A: Users report replacing purchases of disposable bottles with refills. B: The station is metallic. C: The mayor likes water. D: The park has benches. E: Installation took one day.

Reveal answer & reasoning

A

Substitution is the mechanism needed for reduced disposable-bottle use. The other facts are background unless an additional connection is supplied.

ORIGINAL PRACTICE · 2

8. A publisher predicts that lowering price will increase total revenue. Why can evidence of more copies sold still be insufficient?

Reveal answer & reasoning

The increase in units may not offset the lower revenue per copy.

At $20×100 copies revenue is $2,000; at $10×150 it is $1,500. A demand increase strengthens a volume claim but does not by itself establish higher revenue.

KEEP THE BIG IDEAS

Quick revision

  • Strengthening increases support; weakening reduces support. Neither must settle the truth.
  • Test the gap that matters to the exact conclusion.
  • Selection, common trends and denominator changes can explain persuasive-looking data.
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