Why the type of conclusion matters
Finding the conclusion is step one; reading its type is step two — and the type predicts the gap. A recommendation, a prediction, a causal claim, a comparison, and a value judgment each lean on a different unstated assumption, so the kind of conclusion tells you in advance what the right strengthen, weaken, or assumption answer will target.
Most students treat every conclusion the same: find it, then read answer choices and hope one clicks. But once you can label the conclusion's type, you can predict the vulnerability before you read the choices — which makes pre-phrasing and elimination far faster.
This guide walks through the five conclusion types that cover almost every Logical Reasoning argument, with the signal words for each and the assumption each one quietly depends on.
Recommendations and prescriptions ("should")
A recommendation says someone ought to do something. Its hidden assumptions are that the action is feasible, that it will actually produce the intended benefit, and that no significant downside or better alternative outweighs it.
Signal words: should, ought to, must, the best course is, we recommend. When you see them, expect the gap to live in feasibility or in a missing comparison: maybe the plan helps with one problem but creates a worse one, or maybe an unconsidered option would work better.
To weaken a recommendation, introduce a cost, a side effect, or a superior alternative. To strengthen it, rule those out. The conclusion type points you straight at the family of right answers.
Predictions ("will")
A prediction claims something will happen in the future. Its core assumption is that the conditions making the prediction reasonable will hold — that nothing changes the trend and no intervening factor disrupts it.
Signal words: will, is likely to, is going to, expects, by next year. The vulnerability is almost always a changed condition or an overlooked factor: the past pattern may not continue, or a new variable may interrupt it.
Weaken a prediction by showing a relevant condition is about to change; strengthen it by confirming the conditions are stable. Predictions also pair naturally with causal reasoning, since many forecasts assume a cause will keep producing its effect.
Causal claims ("X causes Y")
A causal conclusion says one thing produces another. It assumes there is no alternative cause, that the relationship is not reversed, and that the link is not mere coincidence — the three classic gaps in any correlation-to-causation argument.
Signal words: causes, leads to, produces, is responsible for, results in, because of. The right answer on a weaken question typically supplies an alternative explanation; on a necessary-assumption question, it rules one out.
Causal conclusions are the most heavily tested type, which is why a deep grasp of correlation-versus-causation pays off across flaw, weaken, strengthen, and assumption questions alike.
Comparisons and value judgments
A comparison says one thing is more, less, or better than another. It assumes a fair, common basis of comparison — that the two things were measured the same way and that no hidden difference explains the gap. Attack a comparison by showing the basis is uneven; defend it by confirming the basis is the same.
A value judgment says something is good, bad, justified, or unethical. It assumes a standard by which to judge — and the argument often never states that standard. The gap is the missing criterion: good by what measure? Principle questions frequently sit on top of value-judgment conclusions, supplying or testing the standard.
| Conclusion type | Signal words | Usual hidden assumption |
|---|---|---|
| Recommendation | should, ought, must | Feasible; benefit outweighs cost; no better option |
| Prediction | will, likely, going to | Conditions hold; no intervening factor |
| Causal claim | causes, leads to, due to | No alternative cause, no reversal, not coincidence |
| Comparison | more, less, better than | Fair, common basis of comparison |
| Value judgment | good, wrong, justified | A standard by which to judge |
Worked example
Argument: "The clinic that switched to online scheduling saw no-show rates fall by a third. So other clinics should adopt online scheduling to cut no-shows."
The conclusion is a recommendation ("should adopt"). That immediately tells you the gaps: is online scheduling feasible for other clinics, will it produce the same benefit for them, and is there a hidden cost? The evidence is also a single clinic, so transferability is in play.
A weakener would show the first clinic's drop was caused by something else (a causal gap inside the evidence) or that online scheduling creates a new barrier for some patients (a cost). Reading the conclusion as a recommendation, not just "a claim," hands you the attack before you read the choices.
The common mistake
The common mistake is treating all conclusions as interchangeable factual claims and then searching the answer choices blindly. A "should" conclusion and a "causes" conclusion fail in completely different ways; reading them the same way means you miss the predictable gap.
A second mistake is missing that a conclusion can be a recommendation built on a causal premise. "This causes that, so you should do this" stacks a causal assumption under a recommendation — both gaps are live, and the right answer can hit either.
The fix: after you locate the conclusion, label its type in one word before reading the choices. That single label routes you to the right family of answers and saves you from re-reading five options cold.
Frequently asked questions
How do I tell a prediction from a recommendation?
A prediction says what will happen ("sales will rise"); a recommendation says what someone should do ("the company should expand"). Predictions assume conditions hold; recommendations assume the action is feasible and worth it. The signal words — "will" versus "should" — usually settle it.
Why does the conclusion's type matter for assumption questions?
Because each type depends on a predictable assumption. A causal conclusion assumes no alternative cause; a recommendation assumes feasibility and no better option; a comparison assumes a fair basis. Labeling the type lets you pre-phrase the assumption before reading the choices.
Can a conclusion be more than one type?
Yes. A recommendation often rests on a causal claim ("X causes Y, so you should do X"), which means two assumptions are in play. When that happens, the correct answer may target either gap — so check both.
What about conditional conclusions?
Some conclusions are themselves conditionals ("if the city builds the bridge, traffic will fall"). Treat the if/then structure with conditional logic, and note that the consequent is often a prediction or causal claim with its own assumption layered in.
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