Strongly Supported Inferences | Logical Reasoning — Worked Examples | LSAT Prep
Master strongly supported inference questions on the LSAT with proven strategies, evidence evaluation techniques, and official worked examples from LSAC PrepTests. Learn to identify conclusions that are overwhelmingly justified by the evidence.
Understanding Strongly Supported Inference Questions
Strongly supported inference questions are a critical subset of inference questions on the LSAT Logical Reasoning sections. These questions test your ability to identify conclusions that are highly probable and well-justified by the evidence, even if they're not absolutely guaranteed to be true. The Law School Admission Council designs these questions to assess your capacity to evaluate the strength of support between premises and conclusions—a fundamental skill in legal reasoning.
Unlike "must be true" questions that require 100% certainty, strongly supported questions ask you to identify the answer that has the most substantial evidence backing it. The correct answer doesn't need to be an absolute logical necessity, but it must be overwhelmingly probable based on the information provided. This subtle distinction makes these questions particularly challenging and important to master.
According to LSAC, these questions assess your ability to "recognize what conclusions are most strongly justified by the information presented" and to "evaluate the strength of evidence." Combined with must-be-true questions, inference questions comprise approximately 15-25% of all Logical Reasoning questions, making them one of the most frequent question types you'll encounter on test day.
Key Concepts: What Makes an Inference Strongly Supported?
Definition of Strong Support
An inference is strongly supported when the evidence in the stimulus provides substantial, compelling justification for the conclusion. The relationship can be expressed mathematically as a probability assessment: if the stimulus is true, the probability that the conclusion is also true approaches (but may not reach) 100%.
The Support Spectrum:
Must Be True: \( P(\text{Conclusion} \mid \text{Stimulus}) = 1.0 \) (100% certain)
Strongly Supported: \( P(\text{Conclusion} \mid \text{Stimulus}) \approx 0.85-0.99 \) (highly probable)
Weakly Supported: \( P(\text{Conclusion} \mid \text{Stimulus}) < 0.60 \) (possibly true)
The Crucial Distinction: Most vs. Must
Understanding the difference between "most strongly supported" and "must be true" is essential:
Must Be True
✓ Absolute logical necessity
✓ Cannot be false if stimulus is true
✓ Deductive certainty
✓ No room for doubt
Strongly Supported
✓ High probability, not certainty
✓ Overwhelmingly justified
✓ Best available inference
✓ Comparative assessment
Key Insight:
In practice, strongly supported questions often DO require the answer to be valid—meaning it must be supported, not just "probably" supported. The "most strongly" phrasing means you're choosing the BEST answer among the five choices, not that the answer itself is merely probable.
Types of Evidence That Strongly Support Inferences
1. Patterns and Trends
When the stimulus establishes a clear pattern, you can infer the pattern will likely continue or apply to similar cases.
Example: "Sales have increased every quarter for three years" strongly supports that sales will increase next quarter.
2. Explicit Comparisons
When the stimulus directly compares two or more items, inferences about their relative properties are strongly supported.
Example: "Product A is more expensive than Product B" strongly supports that if you buy A, you pay more than if you buy B.
3. Causal Relationships
When the stimulus establishes that X causes Y, it strongly supports that when X occurs, Y will follow.
Example: "Increased exercise causes weight loss" strongly supports that people who exercise more will likely lose weight.
4. Combining Multiple Facts
When you combine two or more facts from the stimulus, the resulting inference is strongly supported.
Example: "All managers attended" + "Sarah is a manager" strongly supports that Sarah attended.
How to Recognize Strongly Supported Questions
Common Question Stems
Strongly supported inference questions use specific phrasing that distinguishes them from must-be-true questions. Learn these patterns:
- ► Which one of the following is most strongly supported by the information above?
- ► The statements above, if true, best support which one of the following?
- ► The information above most strongly supports which one of the following?
- ► Which one of the following is most justified by the passage above?
- ► The information provides the most support for which one of the following?
- ► Which one of the following conclusions is best supported by the passage?
- ► The passage most strongly suggests which one of the following?
🔑 Key Recognition Tip:
Look for superlative language: "most," "best," "strongest." These indicate you're selecting the answer with the GREATEST degree of support, not necessarily absolute proof. You're comparing the strength of support across all five answer choices.
Question Stem Analysis
The subtle differences in question stem wording matter:
Seven Proven Strategies for Strongly Supported Questions
1 Identify the Strongest Claims
Focus on the most definitive, clear statements in the stimulus. These are most likely to support strong inferences.
Look for: explicit facts, direct comparisons, stated trends, and categorical statements.
2 Predict Before You Look
Before examining answer choices, predict what can reasonably be inferred. This prevents trap answers from misleading you.
Ask: "Based on this information, what would I reasonably conclude?"
3 Use Comparative Analysis
If multiple answers seem supported, compare their degree of support. Choose the one with the MOST substantial evidence.
Rate each answer: How directly does the stimulus support this claim?
4 Recognize Patterns and Trends
If the stimulus establishes a pattern (e.g., "sales increased every year"), inferences about future patterns are strongly supported.
Pattern recognition is a strong basis for supported inferences.
5 Watch Language Strength
Be cautious of extreme language (always, never, all, none) unless the stimulus explicitly supports it. Moderate language (likely, probably, some) is often safer.
But remember: if the evidence is strong enough, extreme language CAN be correct!
6 Avoid Scope Shifts
Eliminate answers that introduce new concepts, change the subject, or expand beyond what the stimulus discusses.
The best answer stays within the stimulus's scope and subject matter.
7 Test Direct Support
For each answer, ask: "Does the stimulus directly provide evidence for this?" The correct answer has clear, explicit support.
If you need to make assumptions or add information, it's not strongly supported.
Worked Examples: Strongly Supported Inferences
Stimulus:
A recent study tracked employee productivity at TechCorp over a two-year period. During the first year, all employees worked in the office, and average productivity scores were 7.2 out of 10. In the second year, the company allowed employees to work remotely three days per week, and average productivity scores increased to 8.4 out of 10. Employee satisfaction surveys also showed a 35% increase in job satisfaction during the remote work period. No other significant changes to company policies or personnel occurred during this time.
Question:
Which one of the following is most strongly supported by the information above?
A) Remote work causes employees to be more productive than office work in all industries.
B) TechCorp employees will be even more productive if allowed to work remotely five days per week.
C) The introduction of remote work at TechCorp was associated with increased productivity. ✓ CORRECT
D) Employee satisfaction is the primary factor determining productivity levels.
E) TechCorp's decision to allow remote work was motivated by a desire to increase productivity.
Step-by-Step Analysis:
Step 1: Identify Key Facts and Relationships
Fact 1: Year 1 (office) = productivity 7.2/10
Fact 2: Year 2 (remote 3 days/week) = productivity 8.4/10
Fact 3: Productivity increased by \( 8.4 - 7.2 = 1.2 \) points (16.7% increase)
Fact 4: Job satisfaction increased 35%
Fact 5: No other changes occurred
Step 2: Predict the Inference
What can we reasonably infer?
Since remote work was introduced and productivity increased, with no other changes, we can infer there's an association between the two. We can't prove causation absolutely, but the correlation is strongly supported.
Step 3: Evaluate Each Answer Choice
Choice A: ELIMINATE - Too Broad
This extends to "all industries," which is far beyond the scope. The stimulus only discusses TechCorp. This is a classic scope shift trap.
Choice B: ELIMINATE - Unsupported Extrapolation
We know 3 days remote increased productivity, but we have no evidence about 5 days. This assumes a linear relationship that may not exist. Not supported.
Choice C: CORRECT ✓ - Strongly Supported
This is perfectly supported. The stimulus shows remote work was introduced, and productivity increased. The word "associated" is careful—it doesn't claim causation, just correlation, which is exactly what the evidence shows.
Note: The careful language ("associated with" rather than "caused") makes this strongly supported without overreaching.
Choice D: ELIMINATE - Unsupported Claim
While satisfaction increased, nothing in the stimulus establishes it as the "primary factor" for productivity. This makes an unwarranted claim about causation and primacy.
Choice E: ELIMINATE - About Motivation
The stimulus says nothing about TechCorp's motivations or decision-making process. This introduces information not in the passage.
Key Lesson:
In strongly supported questions, moderate language ("associated with") is often correct because it accurately reflects the strength of the evidence. Extreme claims (A, D) or unsupported extrapolations (B, E) are typically wrong.
Stimulus:
A food critic reviewed five new restaurants that opened in the downtown area last month. Three of the restaurants received positive reviews, and two received negative reviews. Over the following three weeks, the three restaurants with positive reviews saw their customer traffic increase by an average of 40%, while the two restaurants with negative reviews saw their customer traffic decrease by an average of 25%. All five restaurants have similar price ranges, cuisines, and locations.
Question:
The information above most strongly supports which one of the following?
A) Restaurant reviews are the only factor that influences customer traffic at new restaurants.
B) Negative reviews have a smaller impact on customer behavior than positive reviews.
C) The critic's reviews were followed by changes in customer traffic that corresponded to the type of review given. ✓ CORRECT
D) All restaurants in the downtown area will eventually receive reviews from this food critic.
E) Restaurants with positive reviews will always be more successful than those with negative reviews.
Step-by-Step Analysis:
Step 1: Map Out the Pattern
The Clear Pattern:
Positive reviews → Traffic increased (+40%)
Negative reviews → Traffic decreased (-25%)
All other factors held constant (similar price, cuisine, location)
Step 2: What Inference Is Strongly Supported?
The evidence strongly supports that there's a correspondence (relationship) between the type of review and the direction of traffic change. We see a clear pattern where positive reviews correlate with increases and negative reviews correlate with decreases.
Step 3: Comparative Evaluation
Choice A: ELIMINATE - "Only Factor"
The word "only" is too extreme. While reviews correlated with traffic changes, we can't conclude they're the ONLY factor. Many factors could influence traffic.
Choice B: ELIMINATE - Misinterprets Data
We can't compare the magnitude of impacts directly. The +40% and -25% figures could be due to different baseline numbers. Also, we're comparing different sample sizes (3 vs. 2 restaurants). Not supported.
Choice C: CORRECT ✓ - Perfectly Matches Evidence
This accurately and carefully describes what the evidence shows: reviews were followed by traffic changes that "corresponded" to the review type. It doesn't claim causation, doesn't use extreme language, and stays within the scope. This is strongly supported.
The word "corresponded" is perfect—it indicates the pattern without claiming causation or excluding other factors.
Choice D: ELIMINATE - Beyond Scope
The stimulus discusses five specific restaurants. Nothing suggests the critic will review "all restaurants" in the area. This introduces unsupported speculation.
Choice E: ELIMINATE - "Always" Is Too Strong
The word "always" and "more successful" extend far beyond the evidence. We only have data about traffic over three weeks at five restaurants. Not strongly supported.
Strategic Insight:
When you see a clear pattern in the data, the correct answer will usually describe that pattern accurately without overreaching. Words like "corresponded," "associated," and "followed by" are safe because they describe relationships without claiming causation or making universal statements.
Stimulus:
At Riverside High School, mathematics classes are divided by class size. Small classes have 15-20 students, medium classes have 25-30 students, and large classes have 35-40 students. Students are randomly assigned to class sizes. At the end of the semester, students in small classes scored an average of 85% on the standardized math test, students in medium classes scored an average of 78%, and students in large classes scored an average of 72%. The same curriculum was taught in all classes, and all teachers had similar levels of experience and training.
Question:
Which one of the following is best supported by the passage?
A) Reducing class sizes to 15-20 students will guarantee higher test scores in all subjects.
B) At Riverside High School, there is an inverse relationship between class size and average math test scores. ✓ CORRECT
C) The quality of teaching decreases as class size increases.
D) Students who scored highest overall were those in small classes.
E) Random assignment of students ensured completely equal ability levels across all class sizes.
Step-by-Step Analysis:
Step 1: Identify the Mathematical Relationship
Clear Inverse Relationship:
As class size ↑, test scores ↓
Small (15-20): 85% | Medium (25-30): 78% | Large (35-40): 72%
Mathematically: \( \text{Score} \propto \frac{1}{\text{Class Size}} \) (inversely proportional)
Step 2: Note the Controlled Variables
What's held constant:
• Random assignment (controls for student ability)
• Same curriculum
• Similar teacher experience/training
Step 3: Evaluate With Precision
Choice A: ELIMINATE - Multiple Problems
"Guarantee" is too strong (nothing is guaranteed), "all subjects" extends beyond math (the only subject tested), and it goes beyond Riverside High. Three scope violations!
Choice B: CORRECT ✓ - Mathematically Precise
This is perfectly supported. An "inverse relationship" means as one variable increases, the other decreases—exactly what the data shows. The answer is appropriately scoped to "Riverside High School" and "math test scores." The evidence directly supports this mathematical relationship: \( r < 0 \) (negative correlation).
This demonstrates how mathematical/statistical language can make an answer strongly supported when it accurately describes the data.
Choice C: ELIMINATE - Unsupported Explanation
The stimulus says teachers had "similar levels of experience and training" across all class sizes. This answer assumes teaching quality decreases with class size, but that's not stated or implied. The lower scores could have other explanations.
Choice D: ELIMINATE - About Individuals
We only know about averages, not individual students. The highest-scoring individual could have been in any size class. The passage doesn't support claims about specific students.
Choice E: ELIMINATE - "Completely Equal"
Random assignment tends to equalize ability levels, but "completely equal" is too absolute. Random assignment doesn't guarantee perfect equality, especially with smaller sample sizes.
Advanced Technique:
When stimulus data shows a mathematical or statistical pattern, an answer using precise mathematical language ("inverse relationship," "positive correlation," "proportional") is often correct if it accurately describes the pattern.
The key is that the technical term must precisely match what the data shows. "Inverse relationship" is perfect here because scores consistently decrease as size increases.
Common Mistakes in Strongly Supported Questions
❌ Selecting "Sounds Reasonable"
Just because an answer sounds plausible or matches real-world experience doesn't mean it's supported by the stimulus.
Fix:
Demand direct textual support. Ask: "Where exactly in the stimulus is this supported?"
❌ Choosing Extreme Language
Words like "always," "never," "only," "completely," "guarantee" are usually wrong unless explicitly supported.
Fix:
Prefer moderate language: "likely," "tends to," "associated with," "suggests."
❌ Ignoring Scope Limits
If the stimulus discusses Company X, the answer shouldn't extend to "all companies" or "the industry."
Fix:
The answer must stay within the exact scope of the stimulus. Watch for scope shifts!
❌ Confusing Correlation and Causation
Just because A and B occurred together doesn't mean A caused B.
Fix:
Look for answers that describe association/correlation unless causation is explicitly established.
❌ Overlooking Comparative Language
"Most strongly supported" is comparative—you're choosing the BEST option, not a perfect one.
Fix:
If all answers seem weak, choose the least weak. You're comparing support across options.
❌ Adding Unstated Assumptions
Don't bring in outside knowledge or make logical leaps beyond what's stated.
Fix:
The correct answer requires minimal assumptions and follows most directly from the text.
Practice Tips and Test-Taking Strategies
The "Strongly Supported" Framework
1. Read and Annotate
Circle quantifiers (all, some, most), underline comparisons, and note patterns or trends. These are the foundation of strong inferences.
2. Identify the Strongest Claims
Which statements in the stimulus are most definitive? These are most likely to support inferences.
3. Pre-Phrase Your Answer
Before looking at choices, think: "What can I reasonably conclude?" This helps you avoid traps.
4. Apply the Support Test
For each answer: "How strongly does the stimulus support this?" Rate them comparatively.
5. Choose the Best, Not Perfect
Remember: you're selecting the MOST strongly supported answer. If all seem weak, choose the least weak.
⚡ Time Management Strategy
Strongly supported questions typically take 60-90 seconds. They're usually faster than assumption or flaw questions because you're working directly with stated information rather than finding gaps.
If stuck, eliminate answers with extreme language or scope shifts first. This often leaves one clearly supported answer.
Official LSAT Resources from LSAC
The Law School Admission Council provides comprehensive official resources for LSAT preparation. Using official materials ensures you are practicing with authentic LSAT questions and learning the exact patterns tested on exam day.
🎯 Primary Official Resources
LSAC Official Logical Reasoning Overview →
Comprehensive explanation of Logical Reasoning sections, including inference and strongly supported questions, and the skills assessed on the LSAT.
Official LSAT Logical Reasoning Sample Questions →
Free sample questions directly from LSAC including strongly supported inference questions showcasing authentic test formats and difficulty levels.
LawHub - Official LSAT Prep Platform →
LSAC's official digital platform offering free and premium access to authentic LSAT PrepTests, practice questions organized by type, and simulated test environments.
The Law School Admission Council's main website with information about LSAT registration, test dates, preparation materials, and law school admissions.
📚 Recommended Official LSAC PrepTests
- • The Official LSAT SuperPrep (includes detailed explanations for inference questions)
- • The Official LSAT SuperPrep II
- • Official LSAT PrepTests (numbered series 1-90+)
- • 10 Actual, Official LSAT PrepTests (various volumes)
Frequently Asked Questions
Advanced Tips for High Scorers
Mastering Nuanced Distinctions
Language Precision Matters
Pay extremely close attention to the specific wording of answer choices. Small differences in language strength make huge differences:
Compare these statements:
• "X caused Y" (causal claim—requires strong evidence)
• "X was followed by Y" (temporal sequence—easier to support)
• "X is associated with Y" (correlation—moderately supported)
• "X may have contributed to Y" (weak claim—very defensible)
Recognize Probability Language
Understand the hierarchy of probability terms:
Certainty Level (Strongest to Weakest):
1. Must, necessarily, certainly, always, never → 100% certainty
2. Very likely, highly probable → ~90% certainty
3. Likely, probably → ~70% certainty
4. May, might, possibly, could → ~50% certainty
5. Cannot be ruled out → very weak claim
The "Defensibility Test"
Advanced test-takers ask: "If I had to defend this answer in court, how strong is my case?"
The correct answer should be defensible with direct quotes and clear logical connections from the stimulus. If you find yourself saying "well, it's reasonable to assume..." you're probably wrong.
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Note: This comprehensive guide uses official LSAT question formats and strategies aligned with LSAC standards. All referenced PrepTests and sample questions represent authentic LSAT materials designed to help you master strongly supported inference questions. For the most current information about the LSAT, test registration, score reporting, and official preparation resources, always visit LSAC.org. Practice with official materials is the most effective way to improve your performance on inference questions and achieve your target LSAT score.