Day 20 of 168 · Week 4
Day 20 / 168 Week 4 of 28 Phase 2: Logical Reasoning Deep Dive

Flaw I: The Flaw Vocabulary & Causal Flaws

🕑 ~75 min · Archetype A — LR Deep Dive

Recap: Flaw, Part 1 of 5

Flaw questions ask you to describe what's logically wrong with an argument's reasoning. It's the single largest LR question type by volume, which is why it gets five consecutive days. Today: causal flaws, building directly on Day 9's five standard attacks.

1. The Causal Flaw Vocabulary

"Fails to consider an alternate explanation," "mistakes correlation for causation," "fails to consider that the causal relationship might run in the opposite direction," "draws a causal conclusion from a single instance." These are the answer-choice phrasings that map onto Day 9's five attacks.

2. Matching the Specific Attack

Don't just reach for "correlation isn't causation" reflexively — identify which of the five specific attacks the stimulus is actually vulnerable to, because Flaw answer choices are written to distinguish them precisely.

3. The Recurring Stimulus Shape

"X and Y happened together [or X preceded Y]. Therefore X causes Y." Any time you see this shape, the flaw answer will name one of the five attacks.

Practice items on this page are curriculum-authored in LSAT style; they are not official LSAC questions. Real drilling happens on LawHub — see the assignment below.

Q1. Argument: "Ice cream sales and drowning deaths both rise in summer months. Therefore, ice cream causes drowning." What is the flaw?

A. A scope-shift flaw
B. Fails to consider an alternate cause (hot weather) for both phenomena
C. A conditional-logic flaw
D. A principle-application flaw
Correct answer: B. Both variables likely share a common cause (hot weather driving both swimming and ice cream consumption) — a classic overlooked-alternate-cause flaw.

LawHub drill-tier LR section, untimed. Scan for Flaw stems involving causal language only.

Tomorrow: statistical and sampling flaws — a different flavor of Flaw question, less about causation and more about whether a sample or statistic actually supports the conclusion drawn from it.