Types of Evidence: Complete LSAT Prep Guide
Understanding evidence types is crucial for LSAT Logical Reasoning success. According to LSAC, "determining how additional evidence affects an argument" is one of the ten core skills tested. Mastering evidence identification, evaluation, and application is essential for Strengthen, Weaken, Assumption, and Flaw questions, which together account for approximately 40-45% of all Logical Reasoning questions.
What is Evidence in LSAT Arguments?
Evidence in LSAT Logical Reasoning refers to the information, data, examples, or facts presented to support a conclusion. Evidence serves as premises—the foundation upon which arguments build their claims. Understanding different evidence types and their relative strengths is fundamental to analyzing argument quality and answering questions correctly.
The Evidence-Conclusion Relationship:
\[ \text{Evidence (Premises)} + \text{Assumptions} \rightarrow \text{Conclusion} \]
Evidence quality directly impacts argument strength
💡 Core Principle: The LSAT tests your ability to recognize that different evidence types have different strengths, limitations, and appropriate uses. Statistical evidence from 10,000 subjects supports broader conclusions than a single anecdotal example. Controlled scientific studies provide stronger causal evidence than simple correlations. Expert testimony in a relevant field carries more weight than general authority.
The Major Types of Evidence on the LSAT
The LSAT features eight primary types of evidence, each with distinct characteristics, strengths, and common flaws:
1. Statistical/Empirical Evidence Strong Evidence
Definition: Numerical data, percentages, rates, or quantitative findings from systematic observation, measurement, or research involving multiple cases or subjects.
Characteristics:
- Quantitative: Expressed in numbers, percentages, rates, or statistical measures
- Systematic: Collected through organized, repeatable methods
- Generalizable: When from representative samples, can support broad conclusions
- Verifiable: Can be independently checked and replicated
- Probabilistic: Indicates likelihood, patterns, or tendencies across populations
Statistical Evidence Example:
Argument: "A study of 5,000 adults found that 78% who exercised 30+ minutes daily reported better sleep quality than those who didn't exercise. Therefore, regular exercise improves sleep."
Evidence Type: Statistical evidence from a large-sample study
Strength:
- ✓ Large sample size (5,000 subjects)
- ✓ Quantifiable findings (78%)
- ✓ Clear comparison group (exercisers vs. non-exercisers)
- ✓ Supports generalization about exercise-sleep relationship
Potential Weaknesses:
- ⚠️ Correlation doesn't prove causation (maybe healthier people both exercise more AND sleep better)
- ⚠️ Self-reported data (people might misreport sleep quality)
- ⚠️ Sample representativeness unknown (were all age groups, health statuses included?)
Common Statistical Evidence Patterns:
- Percentages and proportions
- Rates of occurrence or change
- Averages, means, medians
- Correlations between variables
- Comparative statistics (Group A vs. Group B)
- Trends over time
Typical Flaws with Statistical Evidence:
- Unrepresentative samples (surveying only college students to conclude about all adults)
- Insufficient sample size (generalizing from 10 people)
- Confusing correlation with causation
- Misinterpreting percentages vs. absolute numbers
- Cherry-picking data (ignoring contrary statistics)
- Outdated statistics (using 20-year-old data for current claims)
Statistical Evidence Strength Formula:
\[ S = f(n, r, m, c) \]
Where: S = Strength, n = sample size, r = representativeness,
m = methodology rigor, c = conclusion relevance
2. Anecdotal Evidence Weak Evidence
Definition: Individual examples, personal experiences, isolated incidents, or single cases used to support a conclusion.
Characteristics:
- Individual-Based: Focuses on one or few specific cases
- Qualitative: Describes experiences rather than quantifying patterns
- Non-Generalizable: Shows possibility but not probability or typicality
- Subjective: Often based on personal perception or interpretation
- Context-Dependent: Applies to specific circumstances that may not transfer
Anecdotal Evidence Example:
Argument: "My uncle smoked cigarettes for 60 years and lived to age 95 without any health problems. Therefore, smoking isn't actually harmful to health."
Evidence Type: Anecdotal evidence (single personal example)
Why It's Weak:
- ✗ Sample size of ONE cannot support broad generalization
- ✗ This example might be a rare outlier, not typical
- ✗ Contradicts overwhelming statistical evidence showing smoking harm
- ✗ Ignores genetic factors, luck, or other variables
- ✓ Only shows smoking CAN be survived, not that it's LIKELY or safe
Key Distinction: Anecdotal evidence demonstrates POSSIBILITY (one person can smoke and live long) but not PROBABILITY (most people who smoke will have this outcome).
When Anecdotal Evidence Appears:
- "I know someone who..."
- "In my experience..."
- "One case involved..."
- "A friend of mine..."
- "There was a person who..."
The Critical LSAT Principle:
⚠️ Anecdotal Evidence Limitation: On the LSAT, anecdotal evidence establishes that something CAN happen or IS POSSIBLE, but it does NOT establish that something IS LIKELY, TYPICAL, or GENERALLY TRUE. Arguments that use anecdotal evidence to draw broad conclusions commit a classic flaw: hasty generalization or unrepresentative sample.
Valid Uses of Anecdotal Evidence:
- ✓ To show an exception exists (disproving absolute claims like "all" or "never")
- ✓ To demonstrate possibility (at least one case occurred)
- ✓ To generate hypotheses for further research
- ✓ To illustrate or humanize statistical findings (not replace them)
Invalid Uses (Common LSAT Flaws):
- ✗ To support general claims about populations (one person → everyone)
- ✗ To establish likelihood or probability
- ✗ To contradict strong statistical evidence
- ✗ To predict future outcomes broadly
3. Expert Testimony/Authority Moderate-Strong Evidence
Definition: Opinions, statements, or judgments from individuals with specialized knowledge, training, credentials, or experience in a relevant field.
Characteristics:
- Credential-Based: Authority derives from expertise, training, or qualifications
- Field-Specific: Expertise applies only within specialist's domain
- Opinion-Oriented: Represents professional judgment, not necessarily proof
- Evaluative: Requires assessing relevance and credibility
Expert Testimony Example:
Strong Expert Evidence:
"Dr. Sarah Chen, a cardiologist with 25 years of experience and author of numerous peer-reviewed studies on heart disease, states that regular cardiovascular exercise significantly reduces heart disease risk. Therefore, people should engage in regular cardio exercise for heart health."
Why It's Strong:
- ✓ Relevant expertise (cardiologist speaking about heart health)
- ✓ Extensive experience (25 years)
- ✓ Research credentials (published peer-reviewed studies)
- ✓ Direct connection between expertise and claim
Weak Expert Evidence:
"A famous actor, who plays a doctor on TV, recommends this weight-loss supplement. Therefore, the supplement is effective."
Why It's Weak:
- ✗ No relevant expertise (actor, not medical professional)
- ✗ Playing a doctor ≠ being a doctor
- ✗ Potential financial conflict of interest (endorsement deal)
- ✗ Fame doesn't confer medical knowledge
Factors Affecting Expert Testimony Strength:
| Factor | Stronger | Weaker |
|---|---|---|
| Relevance | Expert in exactly the relevant field | Expert in unrelated or tangentially related field |
| Consensus | Multiple experts agree; field consensus | Single expert; experts disagree |
| Credentials | Advanced degrees, research, publications | Self-proclaimed expert; no verifiable credentials |
| Bias | No financial or personal stake | Paid endorsement; personal interest |
| Evidence Base | Opinion backed by data/research | Opinion unsupported by evidence |
| Specificity | Claim within narrow expertise | Claim outside core expertise area |
Common Expert Testimony Flaws on LSAT:
- Irrelevant Expertise: Citing expert outside their field (physicist opining on economics)
- Appeal to Improper Authority: Citing fame, wealth, or general intelligence instead of relevant expertise
- Treating Opinion as Proof: Assuming expert opinion settles the matter without considering evidence
- Ignoring Expert Disagreement: Citing one expert while ignoring others who disagree
- Overlooking Conflicts of Interest: Not considering financial or personal biases
4. Causal Evidence Varies (Context-Dependent)
Definition: Evidence presented to support claims that one thing (X) causes another thing (Y) to occur. Causal claims are among the most common—and most problematic—on the LSAT.
Types of Causal Evidence:
- Correlation: X and Y occur together
- Temporal Sequence: X occurs before Y
- Controlled Experiments: Manipulating X changes Y while controlling other variables
- Mechanism: Explanation of HOW X causes Y
- Elimination: Ruling out alternative causes
Causal Evidence Example:
Weak Causal Claim (Correlation Only):
"Ice cream sales and drowning deaths both increase in summer. Therefore, ice cream sales cause drowning deaths."
Why It's Flawed:
- ✗ Confuses correlation with causation
- ✗ Ignores obvious alternative cause (warm weather causes BOTH)
- ✗ No mechanism explaining how ice cream → drowning
- ✗ Fails to rule out common cause
Stronger Causal Evidence:
"In a randomized controlled trial, researchers gave 1,000 patients either Drug A or a placebo while controlling for all other factors. The Drug A group showed 60% symptom reduction vs. 10% in the placebo group. Therefore, Drug A causes symptom reduction."
Why It's Stronger:
- ✓ Controlled experiment (not just observation)
- ✓ Randomization reduces confounding variables
- ✓ Control group establishes baseline
- ✓ Large sample size
- ✓ Controlled other factors
Causal Reasoning Requirements:
\[ X \rightarrow Y \text{ requires:} \]
\[ \text{1. Correlation (X and Y co-occur)} \]
\[ \text{2. Temporal Sequence (X before Y)} \]
\[ \text{3. No Reverse Causation (Y doesn't cause X)} \]
\[ \text{4. No Common Cause (Z causing both)} \]
\[ \text{5. No Alternative Causes} \]
Classic Causal Reasoning Flaws:
- Correlation ≠ Causation: Assuming that because two things occur together, one causes the other
- Reverse Causation: Getting cause and effect backwards (Y might cause X, not X causing Y)
- Common Cause: Both X and Y caused by third factor Z
- Post Hoc Ergo Propter Hoc: "After this, therefore because of this" - assuming temporal sequence proves causation
- Ignoring Alternative Causes: Failing to rule out other possible explanations
- Oversimplification: Treating complex, multi-causal phenomena as having single causes
Strengthening Causal Arguments:
- ✓ Show controlled studies manipulating the cause
- ✓ Demonstrate mechanism explaining HOW cause produces effect
- ✓ Rule out alternative explanations
- ✓ Show effect doesn't occur without cause
- ✓ Demonstrate temporal sequence
- ✓ Eliminate common cause possibility
Weakening Causal Arguments:
- ✓ Introduce alternative causes
- ✓ Show reverse causation possible
- ✓ Reveal common cause affecting both
- ✓ Show correlation breaks down in some cases
- ✓ Demonstrate effect occurs without supposed cause
5. Analogical Evidence Moderate Evidence
Definition: Evidence that uses comparison to support conclusions: because X is similar to Y in certain ways, and Y has property Z, therefore X probably has property Z too.
Structure of Analogical Reasoning:
\[ X \sim Y \text{ (X similar to Y)} \]
\[ Y \text{ has property } Z \]
\[ \therefore X \text{ probably has property } Z \]
Analogical Evidence Example:
Argument: "City A implemented congestion pricing for downtown traffic and saw a 40% reduction in congestion. City B is similar to City A in size, population density, and public transit availability. Therefore, City B should implement congestion pricing to reduce its congestion."
Evidence Type: Analogical evidence (reasoning from similar case)
Strength Factors:
- ✓ Multiple relevant similarities noted (size, density, transit)
- ✓ Similarities are relevant to traffic/congestion issues
- ✓ Concrete, specific comparison
Potential Weaknesses:
- ⚠️ May be relevant differences not mentioned (driving culture, economic factors)
- ⚠️ Past success doesn't guarantee future success
- ⚠️ Cities might differ in crucial unmeasured ways
Factors Affecting Analogy Strength:
- Relevance of Similarities: Are shared features actually relevant to the property being predicted?
- Number of Similarities: More relevant similarities strengthen the analogy
- Significance of Differences: Are there important differences that break the analogy?
- Specificity: Specific, detailed similarities stronger than vague resemblances
- Causal Connection: Do the similarities actually relate to the predicted outcome?
Common Analogy Flaws on LSAT:
- False Analogy: Comparing things that aren't relevantly similar
- Ignoring Significant Differences: Overlooking crucial dissimilarities
- Superficial Similarity: Basing analogy on irrelevant resemblances
- Overgeneralization: Assuming all similarities from one case transfer to another
According to LSAC: "Reasoning by analogy" is explicitly listed as one of the ten core skills tested in LSAT Logical Reasoning. Expect multiple questions requiring you to evaluate analogical evidence.
6. Survey/Poll Data Moderate Evidence
Definition: Evidence collected through questionnaires, polls, or interviews where people self-report opinions, preferences, behaviors, or experiences.
Characteristics:
- Self-Reported: Relies on what people say about themselves
- Opinion-Based: Often measures beliefs, attitudes, or intentions
- Sampling-Dependent: Conclusions depend on sample quality
- Question-Sensitive: Results influenced by how questions are worded
Survey Evidence Example:
Strong Survey Evidence:
"A random survey of 2,000 registered voters nationwide, conducted by independent pollsters using neutral question wording, found that 65% support the new policy. Therefore, a majority of registered voters support the policy."
Why It's Relatively Strong:
- ✓ Random sampling (reduces selection bias)
- ✓ Large sample (2,000)
- ✓ Appropriate population (registered voters for voting-related conclusion)
- ✓ Independent pollsters (reduces bias)
- ✓ Neutral wording (less leading)
- ✓ Nationwide (representative scope)
Weak Survey Evidence:
"An online survey posted on a political website, which anyone could complete, found that 80% of respondents oppose the new policy. Therefore, most people oppose the policy."
Why It's Weak:
- ✗ Self-selection bias (only motivated people respond)
- ✗ Non-random sample (website visitors aren't representative)
- ✗ Likely politically skewed (political website audience)
- ✗ No controls (people could respond multiple times)
- ✗ Unclear sample size
Survey Quality Factors:
| Quality Indicator | Strong | Weak |
|---|---|---|
| Sampling Method | Random, stratified | Self-selected, convenience |
| Sample Size | Large (1,000+) | Small (< 100) |
| Representativeness | Matches target population | Skewed, homogeneous |
| Question Wording | Neutral, clear, unbiased | Leading, confusing, loaded |
| Response Rate | High (70%+) | Low (< 30%) |
| Administration | Professional, independent | Amateur, interested party |
Common Survey Evidence Flaws:
- Unrepresentative Sample: Survey group doesn't match population
- Selection Bias: Only certain types of people respond
- Question Bias: Wording influences answers
- Social Desirability Bias: People answer based on what's socially acceptable, not truth
- Behavior-Intent Gap: What people say they'll do ≠ what they actually do
- Low Response Rate: Non-respondents might differ systematically from respondents
7. Scientific Studies/Controlled Experiments Strong Evidence
Definition: Evidence from systematic research using scientific methods, typically involving controlled conditions, random assignment, manipulation of variables, and rigorous measurement.
Characteristics:
- Controlled Conditions: Researchers control variables to isolate effects
- Random Assignment: Participants randomly assigned to groups
- Replicable: Methods documented so others can verify
- Objective Measurement: Uses standardized, verifiable measures
- Peer Review: Often evaluated by other experts before publication
Scientific Study Example:
Argument: "In a double-blind, randomized controlled trial published in a peer-reviewed medical journal, 1,500 participants with condition X were randomly assigned to receive either Treatment A or a placebo. Neither participants nor researchers knew who received which until the study ended. After 6 months, the Treatment A group showed 70% improvement compared to 15% in the placebo group, with results statistically significant at p < 0.01. Therefore, Treatment A effectively treats condition X."
Why This Is Strong Evidence:
- ✓ Randomized controlled trial (gold standard design)
- ✓ Double-blind (eliminates bias from both participants and researchers)
- ✓ Placebo control (establishes baseline, controls for placebo effect)
- ✓ Large sample (1,500)
- ✓ Statistically significant results (p < 0.01 means results unlikely due to chance)
- ✓ Peer-reviewed (vetted by experts)
- ✓ Specific, quantifiable outcomes (70% vs. 15%)
Hierarchy of Scientific Evidence Strength:
- Systematic Reviews/Meta-Analyses: Comprehensive analysis of multiple studies (strongest)
- Randomized Controlled Trials (RCTs): Experimental design with random assignment and controls
- Cohort Studies: Following groups over time, comparing outcomes
- Case-Control Studies: Comparing people with/without outcomes, looking back at exposures
- Cross-Sectional Studies: Observational snapshots at one point in time
- Case Reports/Series: Descriptions of individual or small groups of cases (weakest controlled evidence)
Scientific Study Quality Indicators:
- ✓ Random assignment to groups
- ✓ Control/comparison group
- ✓ Blinding (single, double, or triple blind)
- ✓ Large, representative sample
- ✓ Replication by independent researchers
- ✓ Statistical significance
- ✓ Peer review and publication
- ✓ Transparent methodology
- ✓ Controlling for confounding variables
8. Historical/Factual Evidence Strong for Facts, Variable for Predictions
Definition: Evidence based on documented past events, established facts, historical records, or verified occurrences.
Characteristics:
- Documented: Recorded in reliable sources
- Verifiable: Can be confirmed through records, archives, or multiple sources
- Factual: Describes what actually happened, not opinions
- Context-Dependent: Interpretation may vary, but facts remain
Uses of Historical Evidence:
- Establishing that something occurred in the past
- Supporting claims about historical patterns or trends
- Providing analogies or precedents for current situations
- Predicting future outcomes based on past patterns
Strength vs. Weakness:
- ✓ Strong for establishing past facts: "World War II ended in 1945" is verifiable historical fact
- ⚠️ Variable for current/future predictions: "Because X happened in the past, it will happen again" requires assumption that conditions remain similar
⚠️ Common Assumption with Historical Evidence: Arguments using historical evidence to predict the future assume that past conditions, patterns, or causal relationships will continue. This assumption can be challenged by showing how current circumstances differ from historical ones.
Comparing Evidence Types: Relative Strength
Understanding relative evidence strength helps you evaluate arguments and predict correct answers on Strengthen/Weaken questions:
| Evidence Type | General Strength | Best For | Limitations |
|---|---|---|---|
| Controlled Scientific Studies | Very Strong | Establishing causation, supporting generalizations | Expensive, time-consuming; may lack real-world applicability |
| Statistical Evidence | Strong | Showing patterns, probabilities, trends | Correlation ≠ causation; depends on sample quality |
| Expert Consensus | Moderate-Strong | Supporting claims within expert's field | Experts can disagree; opinion not proof |
| Large-Sample Surveys | Moderate | Gauging opinions, preferences, self-reported behavior | Self-report bias; behavior-intent gap |
| Analogical Evidence | Moderate | Predicting outcomes based on similar cases | Depends on relevance of similarities; differences matter |
| Historical Precedent | Moderate | Showing past patterns; establishing facts occurred | Past ≠ future; conditions change |
| Single Expert Opinion | Weak-Moderate | Expert insight in relevant field | Other experts may disagree; potential bias |
| Anecdotal Evidence | Weak | Showing possibility; generating hypotheses | Can't generalize; not typical; sample of one |
Evidence Strength Hierarchy:
\[ \text{RCT} > \text{Stats} > \text{Surveys} > \text{Expert} > \text{Analogy} > \text{Anecdote} \]
Generally: Controlled experiments > Large-sample data > Self-reports > Authority > Comparison > Single examples
Evidence Evaluation Framework
Use this systematic framework to evaluate any evidence type on the LSAT:
The 7-Question Evidence Evaluation Method
- What type of evidence is this? (Statistical, anecdotal, expert, causal, analogical, survey, scientific, historical)
- Is the evidence relevant to the conclusion? Does it actually address the specific claim being made?
- Is the evidence sufficient? Is the sample size, scope, or amount adequate to support the conclusion?
- Is the evidence representative? Does it accurately reflect the population or situation about which conclusions are drawn?
- What does the author assume about this evidence? What must be true for this evidence to support the conclusion?
- What are the evidence's limitations? What inherent weaknesses does this type of evidence have?
- How could this evidence be strengthened or weakened? What additional information would make the argument stronger or weaker?
Applying Evidence Analysis to LSAT Questions
Strengthen Questions with Evidence
Strengthen questions ask you to find evidence that makes the conclusion more likely. Different evidence types strengthen in different ways:
Strengthening Techniques by Evidence Type
- For Statistical Claims: Add larger samples, more representative data, or corroborating studies
- For Causal Claims: Add controlled experiments, eliminate alternatives, show mechanism
- For Analogies: Show additional relevant similarities, minimize differences
- For Expert Claims: Add expert consensus, relevant credentials, supporting data
- For Generalizations: Show pattern holds across diverse cases, larger sample
- For Predictions: Show past patterns continue, conditions remain similar
Weakening Techniques by Evidence Type
- For Statistical Claims: Show unrepresentative samples, selection bias, small size
- For Causal Claims: Introduce alternative causes, reverse causation, common cause
- For Analogies: Highlight relevant differences, show dissimilarities
- For Expert Claims: Show expert disagreement, irrelevant expertise, bias
- For Generalizations: Show exceptions, atypical examples, failed predictions
- For Predictions: Show conditions changed, past patterns broke
Complete Evidence Analysis Example:
Argument:
"A survey of 50 customers who visited our store last Tuesday found that 80% were satisfied with their experience. Therefore, most of our customers are satisfied."
Evidence Analysis:
- Type: Survey data
- Sample Size: 50 (relatively small)
- Sampling Method: One day only (Tuesday) - not random or representative
- Time Frame: Single day - doesn't represent typical patterns
- Selection Bias: Only customers who visited (not those who stopped coming due to dissatisfaction)
Assumptions:
- Tuesday customers are representative of all customers
- Last Tuesday was a typical day
- Customers who visit are representative of all customers (including former customers)
- 50 is a sufficient sample
How to Strengthen:
- ✓ Show the survey covered multiple days/weeks
- ✓ Increase sample size significantly
- ✓ Show random sampling across all customer types
- ✓ Include former customers or non-repeat visitors
How to Weaken:
- ✓ Show Tuesday is atypical (special sale day, best staff working)
- ✓ Reveal dissatisfied customers stop visiting (selection bias)
- ✓ Show 50 is too small to represent thousands of customers
- ✓ Indicate response bias (only happiest customers responded)
Practice Resources for Evidence Types
Official LSAC Practice Materials
LawHub - Official LSAT Prep:
What to Practice:
- Strengthen Questions: Practice identifying what type of evidence would make arguments stronger
- Weaken Questions: Practice recognizing evidence flaws and contrary evidence
- Flaw Questions: Practice identifying problems with evidence types (unrepresentative samples, anecdotal generalization, etc.)
- Assumption Questions: Practice identifying what authors assume about their evidence
Official Resources:
- Free Official LSAT Prep: Practice questions with various evidence types
- LawHub Advantage ($115/year): 75+ PrepTests with hundreds of arguments featuring all evidence types
- LSAC Logical Reasoning Description: Official Skills Tested
- Official Sample Questions: LSAC Sample Questions
Progressive Practice Plan
6-Week Evidence Mastery Schedule
Week 1: Evidence Type Recognition
- Study all 8 evidence types and their characteristics
- Practice identifying evidence types in 30-40 arguments
- Create flashcards for evidence type indicators
- Learn strength hierarchy
Week 2: Evidence Evaluation
- Practice the 7-Question Evidence Evaluation Method on 20-30 arguments
- Focus on identifying assumptions about evidence
- Analyze evidence limitations in arguments
- Compare relative strength of different evidence types
Week 3-4: Strengthen Questions
- Complete 40-50 Strengthen questions from official PrepTests
- Practice matching evidence types to strengthen needs
- Learn to predict strengtheners before reading answer choices
- Identify why wrong answers don't strengthen (irrelevant, weakens, neutral)
Week 5: Weaken Questions
- Complete 40-50 Weaken questions from official PrepTests
- Practice identifying evidence vulnerabilities
- Learn common evidence attack patterns
- Pre-phrase weakeners before reading choices
Week 6: Integration & Flaw Questions
- Complete 30-40 Flaw questions focusing on evidence problems
- Practice identifying evidence assumptions in Assumption questions
- Take full LR sections applying evidence analysis to all question types
- Review and consolidate evidence evaluation skills
Frequently Asked Questions
The LSAT features multiple types of evidence in Logical Reasoning arguments: Statistical/Empirical Evidence (numerical data from studies or research), Anecdotal Evidence (individual examples or personal experiences), Expert Testimony (opinions from authorities or specialists), Causal Evidence (claims about cause-and-effect relationships), Analogical Evidence (comparisons to similar situations), Survey/Poll Data (self-reported information from questionnaires), Scientific Studies (controlled experiments or systematic research), and Historical/Factual Evidence (past events or established facts). According to LSAC, "determining how additional evidence affects an argument" is one of the ten core skills tested in Logical Reasoning, making evidence evaluation central to LSAT success. Each evidence type has different strengths, appropriate uses, limitations, and typical flaws that appear repeatedly in LSAT arguments. Understanding these distinctions helps you evaluate argument quality and predict correct answers on Strengthen, Weaken, Assumption, and Flaw questions.
Statistical evidence uses numerical data from systematic observation, measurement, or research involving large samples to establish patterns, probabilities, or likelihood across populations. It's generally stronger because it's repeatable, verifiable, objective, and represents broader groups. Statistical evidence answers "How often?" "How likely?" and "What percentage?" Anecdotal evidence is based on individual examples, personal experiences, isolated incidents, or single cases—it shows that something is POSSIBLE but doesn't establish how LIKELY, PROBABLE, or TYPICAL it is. On the LSAT, the key distinction: statistical evidence supports generalizations and predictions about groups; anecdotal evidence only demonstrates possibility through single examples. A common LSAT flaw is using anecdotal evidence (one person's experience) to draw broad conclusions that require statistical support—this is called hasty generalization or unrepresentative sample. For example, "My grandfather smoked and lived to 95, therefore smoking isn't harmful" uses anecdotal evidence inappropriately to contradict statistical evidence. Statistical evidence is objective and independently verifiable, while anecdotal evidence is subjective, context-dependent, and can't be generalized. The LSAT frequently tests whether you recognize when arguments inappropriately generalize from anecdotal evidence or when they need statistical evidence to support their claims.
Evaluate LSAT evidence strength using these systematic criteria: 1) Evidence Type - Controlled scientific studies and statistical data are generally stronger than anecdotal examples or surveys. 2) Sample Size - Larger samples provide stronger evidence than small samples; a study of 10,000 participants is stronger than one with 10. 3) Representativeness - Evidence from representative, random samples generalizes better than biased or selective samples. 4) Relevance - Evidence must directly relate to the specific conclusion being drawn; strong but irrelevant evidence doesn't help. 5) Recency - Recent evidence is usually stronger than outdated data for current claims, especially in rapidly changing fields. 6) Source Credibility - Expert testimony from relevant fields is stronger than general authority or celebrity endorsement. 7) Methodology - Controlled experiments are stronger than observational studies; randomized trials are stronger than surveys; double-blind studies eliminate bias. 8) Consistency - Multiple independent pieces of supporting evidence are stronger than a single source. 9) Specificity - Specific, detailed evidence is stronger than vague generalizations. The LSAT tests whether you recognize when evidence is insufficient, inappropriately generalized, unrepresentative, irrelevant to the conclusion, or flawed in methodology.
Causal evidence on the LSAT is evidence presented to support a cause-and-effect claim—that X causes Y. LSAT arguments frequently make causal claims based on various types of evidence: correlation (X and Y occur together), temporal sequence (X precedes Y), controlled studies (manipulating X changes Y while controlling other variables), mechanism explanation (describing HOW X causes Y), or elimination of alternatives (ruling out other possible causes). The key LSAT insight: causal claims require strong evidence ruling out alternative explanations, reverse causation, and common causes. Mere correlation is weak causal evidence because correlation doesn't prove causation. Common causal reasoning flaws include: assuming correlation proves causation, failing to eliminate alternative causes, confusing cause and effect (reverse causation where Y actually causes X), overlooking potential common causes (Z causing both X and Y), and post hoc reasoning (assuming temporal sequence proves causation). Strengthening causal arguments requires evidence eliminating alternatives, demonstrating mechanism, or showing controlled manipulation of cause produces effect. Weakening causal arguments requires evidence showing alternative causes, reverse causation possibility, common cause affecting both, or cases where correlation breaks down (X without Y, or Y without X).
Expert testimony is evidence based on the opinion, judgment, or authority of someone with specialized knowledge, training, credentials, or experience in a relevant field. On the LSAT, expert testimony appears as statements from researchers, doctors, scientists, economists, engineers, or other specialists used to support conclusions. Key considerations for expert evidence strength: 1) Relevance of Expertise - The expert's field must directly relate to the claim being made (a medical doctor's opinion on economic policy isn't strong evidence; their opinion on medical treatments is). 2) Scope of Knowledge - Experts are authoritative only within their specialty; a physics expert isn't automatically an expert on biology. 3) Consensus vs. Individual Opinion - Expert consensus (multiple experts agreeing) is much stronger than a single expert opinion, especially if other experts disagree. 4) Potential Bias - Financial conflicts of interest, employment relationships, or personal stakes weaken expert testimony. 5) Evidence Base - Expert opinions supported by data and research are stronger than unsupported assertions. Common LSAT flaws with expert testimony: citing irrelevant expertise (expert outside their field), appeal to improper authority (citing fame or wealth instead of relevant credentials), treating expert opinion as proof rather than evidence, assuming experts can't disagree, or overlooking conflicts of interest that might bias the expert.
Analogical evidence uses comparison to support conclusions: because X is similar to Y in certain ways, and Y has property Z, therefore X probably has property Z too. LSAT arguments use analogies to predict outcomes, justify policies, support claims, or argue for similar treatment based on similar situations, cases, or examples. The strength of analogical evidence depends on: 1) Relevance of Similarities - The shared features must be relevant to the property being predicted; superficial similarities don't support conclusions. 2) Significance of Differences - Important, relevant differences weaken analogies even if some similarities exist. 3) Number of Relevant Similarities - More relevant similarities strengthen analogies; one similarity is weak. 4) Specificity - Specific, detailed similarities are stronger than vague resemblances. 5) Causal Connection - The similarities must actually relate to the predicted outcome. Common LSAT flaws with analogies: false analogies (comparing situations that aren't relevantly similar), ignoring significant differences (overlooking crucial dissimilarities), superficial similarity (basing conclusions on irrelevant resemblances), or overgeneralization (assuming all features transfer from one case to another). To strengthen analogical arguments, show additional relevant similarities and minimize differences. To weaken them, highlight significant relevant differences or show dissimilarities in key aspects. According to LSAC, "reasoning by analogy" is explicitly listed as one of the ten core skills tested in LSAT Logical Reasoning.
Survey data on the LSAT is evidence based on self-reported information collected through questionnaires, polls, or interviews. Surveys appear in arguments about opinions, preferences, behaviors, beliefs, or intentions. Survey evidence strength depends on: 1) Sample Size - Larger samples (1,000+) generally provide stronger evidence than small samples (less than 100). 2) Representativeness - The surveyed group must represent the population about which conclusions are drawn; surveying college students can't support conclusions about all adults. 3) Sampling Method - Random, stratified sampling is stronger than self-selected convenience samples. 4) Question Wording - Neutral, unbiased, clear questions produce more reliable results than leading, loaded, or ambiguous questions. 5) Response Rate - Low response rates suggest possible selection bias (only motivated people respond). 6) Honesty/Accuracy - Self-reports may be inaccurate due to social desirability bias, memory errors, or dishonesty. Common LSAT flaws with surveys: unrepresentative samples (surveying website visitors to conclude about general population), selection bias (only certain types of people respond), question bias (wording influences responses), social desirability bias (people answer what's socially acceptable, not truthful), or behavior-intent gap (what people say they'll do doesn't match what they actually do). Surveys are generally weaker than controlled studies but stronger than individual anecdotes.
Evidence is relevant on the LSAT when it directly relates to and has logical bearing on the specific conclusion being drawn—it makes the conclusion more or less likely. Evidence is irrelevant when it doesn't actually support or undermine the particular claim made, even if it's true, interesting, or related to the general topic. Key relevance factors: 1) Scope Match - Evidence must address the same scope as the conclusion (evidence about cats doesn't support conclusions about all animals; evidence about adults doesn't support conclusions about children). 2) Time Frame Match - Evidence from the relevant time period (past data may not support future predictions without additional assumptions about continuity). 3) Context Match - Evidence must apply to the specific context of the conclusion (evidence from one country may not apply to another with different conditions). 4) Property Match - Evidence about one characteristic doesn't necessarily support conclusions about different characteristics (evidence that X is popular doesn't support conclusions that X is effective). Common irrelevance patterns: evidence about different groups, evidence about different outcomes, evidence about different time periods, or evidence about correlation used to support causation conclusions. The LSAT frequently includes answer choices with strong but irrelevant evidence as trap answers. Always ask: Does this evidence actually make the SPECIFIC conclusion more or less likely, or does it just relate to the general topic?
To strengthen LSAT arguments with evidence, add information that makes the conclusion MORE likely to be true by: 1) Supporting Assumptions - Provide evidence confirming what the author takes for granted or assumes without stating. 2) Eliminating Alternatives - Rule out other explanations, causes, or possibilities that would undermine the conclusion. 3) Increasing Sample Size or Scope - Show patterns hold across larger, more diverse, or more representative groups. 4) Establishing Causation - Provide controlled studies, temporal sequence evidence, mechanism explanations, or elimination of alternative causes. 5) Confirming Analogies - Show additional relevant similarities between compared situations or minimize relevant differences. 6) Adding Expert Consensus - Provide supporting testimony from multiple relevant authorities or show field-wide agreement. 7) Demonstrating Representativeness - Show samples or examples are typical, not outliers or exceptions. 8) Providing Mechanism or Explanation - Explain HOW or WHY the claim would be true. Important: Strengthen answers don't have to PROVE the conclusion definitively, just make it MORE LIKELY or MORE REASONABLE. According to LSAC, "determining how additional evidence affects an argument" is a core tested skill. Practice identifying assumptions first, then look for evidence that would support those assumptions. Use official LSAC PrepTests from LawHub at lsac.org/lsat/prep to practice Strengthen questions with various evidence types from real LSAT administrations.
To weaken LSAT arguments with evidence, add information that makes the conclusion LESS likely to be true by: 1) Attacking Assumptions - Provide evidence showing what the author assumes is false, questionable, or unreliable. 2) Introducing Alternatives - Show other explanations, causes, or possibilities that make the conclusion less certain. 3) Revealing Sample Problems - Demonstrate samples are unrepresentative, biased, too small, or atypical. 4) Breaking Causal Claims - Show correlation without causation, reverse causation (effect causes supposed cause), or common causes (third factor causes both). 5) Undermining Analogies - Highlight significant relevant differences between compared situations. 6) Showing Expert Disagreement - Provide contrary expert opinions, show field disagreement, or reveal irrelevant expertise or bias. 7) Demonstrating Atypicality - Reveal that examples or data points are outliers, exceptions, or not typical cases. 8) Introducing Contrary Evidence - Provide data, studies, or examples that contradict the conclusion. Important: Weaken answers don't have to DISPROVE the conclusion completely, just make it LESS LIKELY or LESS CERTAIN. Common weakening patterns: For causal arguments, introduce alternative causes. For generalizations based on samples, show samples are unrepresentative. For predictions based on past patterns, show conditions changed or patterns don't continue. For analogies, show relevant differences. Always use official LSAC materials from LawHub at lsac.org/lsat/prep to practice Weaken questions with authentic evidence patterns from real LSAT tests.
Strong evidence on the LSAT is evidence that reliably supports conclusions, generalizes appropriately, comes from rigorous methodology, and has minimal inherent limitations. Characteristics include: large representative samples, controlled conditions, objective measurement, replicability, relevant expertise, statistical significance, and direct relevance to conclusions. Examples: randomized controlled trials, large-scale statistical studies from representative samples, consensus of relevant experts, controlled experiments with proper methodology. Weak evidence has significant limitations that reduce its ability to support conclusions. Characteristics include: small or unrepresentative samples, lack of controls, subjective measurement, inability to replicate, irrelevant authority, anecdotal nature, or indirect relevance. Examples: single anecdotal cases, small non-random samples, opinions from non-experts, surveys with biased samples or questions, correlation without causation, analogies with significant differences. Key LSAT principles: Evidence type matters—controlled studies trump anecdotes. Sample size and representativeness matter—10,000 subjects stronger than 10. Methodology matters—randomized trials stronger than surveys. Relevance matters—strong evidence in the wrong area doesn't help. Context matters—what's strong for one type of claim may be weak for another. The LSAT tests your ability to recognize when evidence is insufficient for the conclusion drawn, even if it's not completely worthless.
Common Evidence-Based Flaws on the LSAT
Recognizing these common patterns helps you anticipate Flaw questions and understand argument weaknesses:
Top 10 Evidence Flaws on the LSAT
1. Hasty Generalization / Unrepresentative Sample
Drawing broad conclusions from insufficient, unrepresentative, or biased samples. Using anecdotal evidence (one or few examples) to support general claims about populations.
Example: "Three people in my office got flu shots and still got sick, therefore flu shots don't work."
2. Correlation ≠ Causation
Assuming that because two things occur together or are correlated, one must cause the other. Fails to consider reverse causation, common causes, or coincidence.
Example: "Crime decreased when police started wearing body cameras, therefore body cameras reduce crime."
3. False Analogy
Comparing two situations that aren't relevantly similar, or ignoring significant differences that break the analogy.
Example: "Raising children is like gardening—both require water and care, so child-rearing methods should follow gardening principles."
4. Appeal to Irrelevant Authority
Citing someone as an authority who lacks relevant expertise in the specific area being discussed.
Example: "Famous actor X recommends this medical treatment, so it must be effective."
5. Biased Sample / Selection Bias
Drawing conclusions from samples that systematically exclude relevant groups or over-represent certain types.
Example: "We surveyed our current customers and 95% are satisfied, proving we have excellent service." (Ignores former customers who left due to dissatisfaction.)
6. Insufficient Sample Size
Using a sample too small to support the conclusion drawn, especially for statistical or probabilistic claims.
Example: "We tested the drug on 5 people and saw improvement, so it works for the general population."
7. Overlooking Alternative Explanations
Failing to consider or rule out other possible causes, reasons, or explanations for observed phenomena.
Example: "Sales increased after our ad campaign, therefore the campaign caused the increase." (Ignores seasonal factors, competitor closures, economic conditions, etc.)
8. Outdated or Irrelevant Evidence
Using evidence from the wrong time period, population, or context to support current claims.
Example: "Studies from the 1950s show this approach works, so we should use it today." (Ignores changed conditions.)
9. Confusing Possibility with Probability
Treating evidence that something CAN happen as evidence that it IS LIKELY to happen.
Example: "Someone won the lottery playing these numbers, so playing these numbers is a good strategy."
10. Survey/Self-Report Problems
Overlooking inherent limitations of self-reported data: social desirability bias, memory errors, dishonesty, or gaps between stated intentions and actual behavior.
Example: "A survey found 80% of people say they exercise regularly, therefore most people are physically active." (People overreport socially desirable behaviors.)
Evidence and LSAT Question Types
Evidence analysis applies across multiple LSAT Logical Reasoning question types:
| Question Type | How Evidence Matters | Frequency |
|---|---|---|
| Strengthen Questions | Find evidence that makes conclusion more likely; support assumptions; eliminate alternatives | ~14% |
| Weaken Questions | Find evidence that makes conclusion less likely; attack assumptions; introduce alternatives | ~14% |
| Flaw Questions | Identify problems with evidence: insufficient, unrepresentative, irrelevant, or inappropriately used | ~16% |
| Assumption Questions | Identify what author assumes about evidence: representativeness, accuracy, relevance, sufficiency | ~10% |
| Evaluate Questions | Determine what additional evidence would help evaluate argument strength | ~1-2% |
| Paradox/Resolve Questions | Find evidence that explains seemingly contradictory evidence or findings | ~4-5% |
| Method of Reasoning | Describe how author uses evidence (analogy, statistical study, expert testimony, etc.) | ~6-7% |
💡 Combined Impact: Questions requiring evidence analysis account for approximately 60-70% of all LSAT Logical Reasoning questions. Mastering evidence evaluation is therefore essential for LSAT success—it's not just useful for a few question types, but fundamental across most of the section.
Advanced Evidence Analysis Tips
Expert-Level Evidence Evaluation Strategies
1. Pre-Phrase Evidence Needs
Before reading answer choices on Strengthen/Weaken questions, predict what type of evidence would help or hurt the argument. Ask: "What would make this more/less likely?" This prevents being swayed by attractive but incorrect answers.
2. Identify the Evidence Gap
Recognize the logical gap between evidence presented and conclusion drawn. What's missing? What's assumed? The gap reveals what additional evidence strengthens and what contrary evidence weakens.
3. Consider Evidence Source
Always evaluate WHO provides the evidence and whether they might be biased. Company-funded studies, interested parties, or non-experts weaken evidence credibility.
4. Watch for Scope Shifts
Evidence about Group A used to conclude about Group B, or evidence about Property X used to conclude about Property Y. Scope shifts signal weak reasoning.
5. Quantify When Possible
Pay attention to specific numbers, percentages, and comparisons. "Most" vs. "some" vs. "a few" matters. "80%" vs. "40%" matters. Precise quantification helps evaluate evidence strength.
6. Consider Alternative Interpretations
Ask whether the evidence could be explained differently. Evidence open to multiple interpretations is weaker than evidence pointing to one conclusion.
7. Apply the Representative Test
For any sample-based evidence, ask: "Is this sample representative of the population about which conclusions are drawn?" Unrepresentative samples are a top LSAT flaw.
8. Use Process of Elimination
On Strengthen/Weaken questions, eliminate answers that are irrelevant (don't affect the conclusion), neutral (don't make it more or less likely), or go the wrong direction (strengthen when you need weaken).
Official Resources for Evidence Mastery
Official LSAC Resources - Use These EXCLUSIVELY:
- LSAC Official Website: LSAC.org - Complete LSAT information
- Official LSAT Prep (LawHub): LawHub Platform - 75+ official PrepTests with authentic evidence patterns
- Logical Reasoning Overview: Official LR Description - LSAC's explanation of tested skills including evidence evaluation
- Sample Questions: Official LR Samples - Free examples with explanations
- LSAT Test Dates: Official Schedule - Registration and test dates
Why Official Materials Matter:
- ✓ Authentic evidence patterns exactly as they appear on test day
- ✓ Accurate difficulty calibration
- ✓ Consistent argument construction methodology
- ✓ Verified answer explanations from test creators
- ✓ Representative distribution of evidence types
Key Takeaways: Evidence Mastery
Essential Principles for LSAT Success
- Evidence Type Matters: Different types have different strengths—statistical beats anecdotal, controlled studies beat surveys, relevant experts beat celebrities
- Sample Quality is Crucial: Large, representative, random samples support stronger conclusions than small, biased, self-selected samples
- Correlation ≠ Causation: Co-occurrence doesn't prove causation—consider alternatives, reverse causation, and common causes
- Relevance is Essential: Strong evidence in the wrong area doesn't help—evidence must directly relate to the specific conclusion
- Anecdotes Show Possibility, Not Probability: Single examples demonstrate something CAN happen, not that it IS LIKELY or TYPICAL
- Expert Testimony Requires Relevant Expertise: Credentials matter only within the expert's field—fame ≠ expertise
- Analogies Depend on Relevant Similarities: Comparisons work only when similarities are relevant to the predicted outcome
- Survey Data Has Inherent Limitations: Self-reports are subject to bias, question wording matters, and representativeness is crucial
- Context and Scope Must Match: Evidence about one group, time, or context may not apply to different groups, times, or contexts
- Evidence Evaluation is Central to LSAT Success: 60-70% of LR questions require analyzing how evidence affects arguments
The Complete Evidence Evaluation Formula:
\[ E_{\text{strength}} = f(\text{type}, n, r, R, M, C) \]
Where: type = evidence type, n = sample size, r = representativeness,
R = relevance, M = methodology, C = credibility
Remember:
\[ \text{Good Evidence} \neq \text{Proof} \]
\[ \text{Evidence Makes Conclusions More/Less Likely} \]
Mastering evidence types and evaluation is fundamental to LSAT Logical Reasoning success. Understanding that statistical evidence supports broader conclusions than anecdotal examples, that causal claims require more than correlation, that expert testimony depends on relevant expertise, and that surveys have inherent limitations will dramatically improve your performance on Strengthen, Weaken, Assumption, and Flaw questions. The key is systematic analysis: identify evidence type, evaluate its quality and relevance, recognize assumptions about the evidence, understand its limitations, and determine how additional evidence would affect the argument. With dedicated practice using official LSAC PrepTests from LawHub, you'll develop the pattern recognition and analytical skills needed to evaluate evidence quickly and accurately under test conditions, significantly improving your LSAT Logical Reasoning score. According to LSAC, "determining how additional evidence affects an argument" is one of ten core tested skills—make evidence mastery a priority in your LSAT preparation.
