Critical Reasoning and Argument Evaluation
Spot correlation-causation and hasty-generalization fallacies, and evaluate what strengthens an argument.
What you'll learn
- Identify a correlation-causation fallacy in an argument
- Identify a hasty generalization from an unreasonably small sample
- Determine what kind of new evidence would genuinely strengthen a causal claim
Prerequisites
Explanation
Critical reasoning questions present a short argument and ask you to evaluate it -- find its flaw, or judge what would strengthen or weaken it. Two fallacies show up constantly enough to learn by name.
Correlation-causation is claiming that because two things happened together (or one followed the other), one must have caused the other. "Ice cream sales and drowning incidents both rise in summer -- so ice cream causes drowning" is the classic example: both are actually caused by a third factor (warmer weather, more swimming), not by each other. The fix is always the same question: is there a plausible alternative explanation for the correlation that the argument hasn't ruled out?
Hasty generalization is drawing a broad conclusion from a sample that's too small or unrepresentative to support it. "I surveyed 4 people and 90% prefer online classes" is a generalization built on a sample of 4 -- nowhere near enough to represent a larger population, even though the percentage sounds precise and authoritative.
Evaluating what strengthens or weakens an argument follows directly from identifying its gap. If an argument claims causation from correlation, evidence that rules out the plausible alternative causes, from a reliable source, genuinely strengthens it -- vague supporting evidence that doesn't address the alternative explanation does not, no matter how relevant it sounds. Evidence that actively points to an alternative cause weakens the argument. The discipline is always the same: name the argument's actual logical gap first, then judge each piece of evidence by whether it closes that specific gap.
Example
Flagging the two most common critical-reasoning fallacies from structured argument data.
function detectFallacy(argument) {
if (argument.claimsCausationFromCorrelation) return 'correlation-causation';
if (argument.generalizesFromSample && argument.sampleSize < 5) return 'hasty-generalization';
return 'none';
}
// detectFallacy({ claimsCausationFromCorrelation: true, sampleSize: 100, generalizesFromSample: false }) -> 'correlation-causation'Guided exercise
Guided exercise
Write detectFallacy(argument), where argument is { claimsCausationFromCorrelation, sampleSize, generalizesFromSample }. Return 'correlation-causation' if it claims causation from correlation, else 'hasty-generalization' if it generalizes from a sample smaller than 5, else 'none'.
Checks: Flags a correlation-causation claim · Flags a hasty generalization from a tiny sample · plus 1 hidden check
Code editor. Press Escape then Tab to leave the editor if keyboard focus becomes trapped. Press Control+Shift+M inside the editor to toggle Tab-key focus trapping.
Stuck? Get a hint.
Independent exercise
Independent exercise
Write strengthensArgument(argument, evidence) where argument is { claimsCausationFromCorrelation } and evidence is { rulesOutAlternativeCause, isFromReliableSource }. Return true only if the argument makes a causal claim AND the evidence both rules out an alternative cause and comes from a reliable source.
Checks: Confirms genuinely strengthening evidence · Rejects evidence from an unreliable source · plus 1 hidden check
Code editor. Press Escape then Tab to leave the editor if keyboard focus becomes trapped. Press Control+Shift+M inside the editor to toggle Tab-key focus trapping.
Stuck? Get a hint.
Common mistakes
- Assuming correlation implies causation without considering a plausible alternative explanation.
- Trusting a percentage or statistic without checking the sample size behind it.
- Judging evidence as 'strengthening' just because it's related to the topic, rather than checking whether it actually closes the argument's specific logical gap.
Knowledge check
Takeaway
Name an argument's specific logical gap before judging any evidence -- only evidence that closes that exact gap actually strengthens it.
Summary
Correlation-causation fallacies mistake co-occurrence for cause; hasty generalizations draw broad conclusions from too-small samples. Evidence only strengthens an argument when it closes the argument's specific gap, such as ruling out an alternative cause from a reliable source.
Your notes
Notes save automatically.
Finished this lesson?
Mark it complete to track your progress and schedule a future review.
AI tutor
The optional AI tutor isn't enabled in this deployment. All lessons, exercises, quizzes, and search work fully without it.