Common Logical Flaws & Fallacies

What You Need to Know

Logical Flaw questions (and many Strengthen/Weaken/NA/Sufficient Assumption questions) hinge on spotting how an argument goes wrong. On the LSAT, most wrong arguments fail in a small set of repeatable ways: bad conditional reasoning, bad causal reasoning, bad sampling, shifting terms/quantifiers, or confusing what was proven.

Your core job: separate premises (support) from conclusion (claim), then name the gap: what must be true for the premises to justify the conclusion?

The “flaw vocabulary” you must recognize
  • Necessary vs. sufficient confusion (especially with conditionals)
  • Correlation vs. causation (and its cousins: reverse causation, confounders)
  • Quantifier shifts (some/most/all; “not all” vs “none”)
  • Scope shifts / new term introduced (conclusion is about something broader/different)
  • Representativeness & sampling (small, biased, self-selected)
  • Attacking the source instead of the claim (ad hominem)
  • Misdescribing an opponent’s view (straw man)
  • Bad comparisons/analogies (ignoring relevant differences)

Critical reminder: a “flaw” answer choice can be worded abstractly. If you can translate it into what happened in the stimulus, it’s a contender.

Step-by-Step Breakdown

Use this for Flaw, Weaken, Strengthen, Necessary Assumption, and Parallel Flaw.

  1. Find the conclusion (the author’s main point).

    • Look for: therefore, thus, hence, so, consequently, clearly.
    • Or ask: “What is the author trying to prove?”
  2. List the premises (the support).

    • Evidence, facts, studies, statistics, comparisons, expert statements.
  3. Put the reasoning in a simple form.

    • Conditional? Causal? Statistical? Analogy? Plan/proposal? Elimination?
  4. Articulate the gap in one sentence.

    • “This assumes ____.”
    • “This confuses ____ with ____.”
    • “This rules out ____ without justification.”
  5. Pre-phrase the flaw (predict it before reading answers).

    • Even a rough prediction helps you avoid trap answers that sound logical.
  6. Match your pre-phrase to an answer choice.

    • Correct answers often use standard phrasing like “takes for granted,” “presumes,” “fails to consider,” “confuses,” “infers… solely from,” etc.
Micro-worked example (conditional)

Stimulus: “If a product is unsafe, it will be recalled. This product was recalled. Therefore it is unsafe.”

  • Form:
    UnsafeRecalled\text{Unsafe} \rightarrow \text{Recalled}
    Recalled\text{Recalled}
    Unsafe\therefore \text{Unsafe}
  • Flaw: Affirming the consequent (treating a necessary condition as sufficient).

Key Formulas, Rules & Facts

A. Conditional reasoning fallacies (super high-yield)

Valid moves

  • Modus Ponens:
    ABA \rightarrow B
    AA
    B\therefore B
  • Modus Tollens:
    ABA \rightarrow B
    ¬B\neg B
    ¬A\therefore \neg A

Invalid moves (classic flaws)

  • Affirming the consequent:
    ABA \rightarrow B
    BB
    A\therefore A
  • Denying the antecedent:
    ABA \rightarrow B
    ¬A\neg A
    ¬B\therefore \neg B
B. Fallacy map (what it is, LSAT tells, how to attack)
Flaw / FallacyWhat it isCommon LSAT “tells”Best attack (what would fix/undercut it)
Necessary vs. sufficientTreats a requirement as a guarantee (or vice versa)“Only if,” “requires,” “must,” “ensures,” “guarantees” misusedIdentify which condition is required vs enough; look for missing link
Affirming consequentConcludes AA from ABA \rightarrow B and BB“Since we observed B, A happened”Show other ways to get BB without AA
Denying antecedentConcludes ¬B\neg B from ABA \rightarrow B and ¬A\neg A“Not A, so not B”Show BB can occur without AA
Causation from correlationTreats correlation as proof of causation“Associated with,” “linked to,” “goes with” → “therefore causes”Alternative cause, reverse cause, coincidence, confounder
Post hocBecause B followed A, A caused B“After,” “since then,” “following”Show other changes occurred; timing alone isn’t causation
Confounding variableIgnores a third factor causing both“People who do X have Y”Introduce plausible common cause
Reverse causationMixes up direction“X is correlated with Y, so X causes Y”Show Y could cause X
Sampling bias / unrepresentativeSample doesn’t represent population“Survey of website visitors,” “volunteers,” “small group”Demand random/representative sample; increase size/diversity
Small sample / anecdoteGeneralizes from too little“In my experience,” “one town,” “three cases”Ask for broader data; show variability
Hasty generalizationOvergeneralizes from limited evidence“Therefore all/most/always” from “some”Point out exceptions; require stronger quantifier support
Quantifier shiftIllegitimately shifts some/most/all“Some” → “Most,” “Not all” → “None”Track quantifiers precisely; require support matching scope
Scope shiftConclusion broader/different topicPremises about A, conclusion about A + BHighlight new element; require premise connecting it
EquivocationKey term changes meaning mid-argumentSame word used differentlyClarify definition; show the switch
Circular reasoningConclusion restates a premise“X is true because X”Demand independent support
Straw manMisrepresents opposing viewOpponent said “some,” author attacks “all”Compare what was said vs attacked
Ad hominemAttacks person/source not claim“She’s biased/immoral, so false”Separate credibility from truth; require evidence about claim
Appeal to authority (weak)Treats authority as decisive when not qualified or context missing“Expert says…” with no field relevanceAsk: qualified? consensus? evidence?
False dilemmaOnly two options when more exist“Either A or B”Provide third alternative; show coexistence
Slippery slopeUnjustified chain of escalating outcomes“If we allow A, soon Z”Demand links between steps; show stopping points
False analogyAssumes because two things share some traits, they share the key trait“Just like…”Identify relevant disanalogy
CompositionParts have property ⇒ whole has it“Each part is light, so the machine is light”Whole can differ due to combination
DivisionWhole has property ⇒ each part has it“Company is rich, so each employee is rich”Parts can differ from whole
Percent vs number / relative vs absoluteConfuses rate with raw count, or % change with total“Higher percentage, so more people”Demand base rates, denominators
Self-selectionPeople choose to be in sample/treatment“Call-in poll,” “opt-in study”Show participants differ systematically

Examples & Applications

Example 1: Causation (classic weaken targets)

Stimulus: “In neighborhoods where more parks were built, crime fell. Therefore, building parks reduces crime.”

  • Type: Correlation → causation.
  • Key insight: Need to consider:
    • Confounder: city also increased policing or economic investment.
    • Reverse causation: city built parks because crime was already falling.
    • Selection: parks built in neighborhoods already trending safer.
  • Best weaken answer shapes: introduces alternative cause, shows timing mismatch, or shows crime fell equally where no parks were built.
Example 2: Necessary vs. sufficient (common flaw answer wording)

Stimulus: “To get licensed, a contractor must pass an exam. Rivera passed the exam, so Rivera is licensed.”

  • Form: LicensedPassed\text{Licensed} \rightarrow \text{Passed} (passing is necessary, not sufficient)
  • Flaw: Treats a necessary condition as sufficient.
  • What would fix: add that passing the exam is enough, or that Rivera completed all other licensing requirements.
Example 3: Quantifier shift + sampling

Stimulus: “A survey of 50 customers found that some prefer Brand A to Brand B. Therefore, most customers prefer Brand A.”

  • Flaws:
    • Some → most (quantifier shift)
    • Small/possibly unrepresentative sample
  • Trap answers: choices that only say “survey size is small” might be incomplete if the bigger problem is the quantifier leap (depends on wording).
Example 4: Equivocation

Stimulus: “A ‘theory’ is just a guess. Evolution is a theory. So evolution is just a guess.”

  • Flaw: “Theory” shifts meaning (scientific theory vs casual guess).
  • Correct flaw answer often says: uses a key term in an inconsistent or ambiguous way.

Common Mistakes & Traps

  1. Boldly labeling a fallacy without matching the stimulus.

    • What goes wrong: You see “correlation” and scream “causation flaw,” but the argument only claims “this is evidence of,” not “this proves.”
    • Fix: Match the strength of the conclusion: “suggests” vs “establishes.”
  2. Mixing up conditional indicators (“only if” vs “if”).

    • What goes wrong: You reverse the arrow.
    • Rule:AA only if BB” means ABA \rightarrow B (B is necessary).
    • Fix: Translate slowly, then look for mistaken reversal.
  3. Choosing an answer that’s true but not the flaw.

    • What goes wrong: You pick a criticism of the evidence that doesn’t explain why the conclusion doesn’t follow.
    • Fix: Ask: “If I fixed this issue, would the reasoning become valid?” If no, it’s not the core flaw.
  4. Falling for “criticizes the author” trap language.

    • What goes wrong: You pick harsh-sounding answers (e.g., “ignores all evidence”) when the stimulus does something milder.
    • Fix: Calibrate: LSAT flaw answers must be accurate, not just negative.
  5. Confusing “attacks source” with “attacks argument.”

    • What goes wrong: You call it ad hominem whenever someone mentions bias.
    • Fix: It’s ad hominem only if the argument goes: “Source is bad → claim is false,” without addressing the claim’s support.
  6. Missing the “new term” in the conclusion (scope shift).

    • What goes wrong: Premises discuss “reducing emissions,” conclusion recommends “banning cars.”
    • Fix: Circle new concepts in the conclusion; ask where they were supported.
  7. Overlooking denominator/base-rate issues in stats.

    • What goes wrong: “More incidents” is inferred from “higher rate” (or vice versa).
    • Fix: Always ask: “Out of how many?” “Compared to what total?”
  8. On Parallel Flaw, matching topic instead of structure.

    • What goes wrong: You pick the choice with similar subject matter rather than the same logical mistake.
    • Fix: Abstract it: replace content with letters, then match the pattern.

Memory Aids & Quick Tricks

Trick / MnemonicWhat it helps you rememberWhen to use it
CARS = Correlation, Alternative cause, Reverse causation, Statistical fluke/SelectionThe main ways to attack causal claimsAny time you see “X is linked with Y, so X causes Y”
NA = “Needs + Assumes”Necessary Assumption answers state what the argument must assumeOn NA questions and when pre-phrasing a flaw
“Only if” = arrow points to the “only” sideTranslate conditionals correctlyAny conditional stimulus
Some/Most/All ladderQuantifier discipline: allmostsome\text{all} \Rightarrow \text{most} \Rightarrow \text{some} (but not backward)Any generalization or survey
New Word = New WorkIf conclusion adds a new concept, you need a premise bridging itGreat for quick flaw spotting
Parts ≠ WholeComposition/division errorsAny argument moving between group and members

Quick Review Checklist

  • Can you underline the conclusion and list premises in under 1515 seconds?
  • Did the argument switch from correlation to causation (or ignore reverse/third-variable explanations)?
  • Did it confuse necessary and sufficient (especially via “only if,” “must,” “requires”)?
  • Did it make an illegal conditional move (affirming consequent / denying antecedent)?
  • Did it shift quantifiers (some → most/all; not all → none)?
  • Did it broaden scope or add a new term in the conclusion?
  • Did it rely on a bad sample (small, biased, self-selected) or an anecdote?
  • Did it equivocate (same word, different meaning) or argue in a circle?
  • For Parallel Flaw: did you match the structure rather than the topic?

You don’t need to memorize every name—just get fast at spotting the pattern and stating the gap clearly.