SAT Reading & Writing – Information and Ideas
Command of Evidence: Quantitative
Interpreting data, graphs, and tables to support claims
Command of Evidence: Quantitative questions test your ability to interpret data from tables, graphs, and charts to determine which quantitative information best supports textual claims or conclusions. On the SAT, you'll analyze numerical data, identify trends and patterns, match data to statements, and determine which evidence from visual sources most directly proves assertions.
Success requires careful reading of both text and data displays, understanding different graph types, recognizing what specific numbers prove versus general trends, and matching quantitative precision to claim scope. These skills aren't just academic—they represent data literacy essential for scientific research, business analysis, policy evaluation, journalism, and informed decision-making in an increasingly data-driven world.
Understanding Quantitative Evidence Questions
Question Formats
Quantitative evidence appears in several presentation styles.
Bar graphs: Comparing categories or groups
Line graphs: Showing trends over time or continuous relationships
Scatter plots: Displaying relationships between two variables
Common task: "Which choice most effectively uses data from the table to complete the statement?"
Reading Data Accurately
Precision matters when interpreting numerical information.
Note units: Percentages vs. raw numbers, thousands vs. millions
Read legends: Identify which line/bar represents what
Verify scale: Linear vs. logarithmic, breaks in axes
Exact vs. approximate: Match precision level to what data shows
Matching Data to Claims
Good quantitative evidence directly supports specific assertions.
Comparisons: "Greater than," "less than" needs comparative data
Trends: "Increasing," "decreasing" requires multiple data points
Proportions: Percentage claims need percentage data or ratios
Scope match: Claim's breadth must match data's coverage
Common Data Interpretation Tasks
What the SAT typically asks you to do with data.
Calculate differences: Determine changes or gaps
Recognize trends: Spot patterns of increase/decrease
Compare groups: Contrast different categories
Support conclusions: Match data to textual claims
Essential Data Interpretation Strategies
Understand Before Analyzing
Read title: Tells you what data represents
Check axes labels: X-axis (horizontal), Y-axis (vertical)
Note units: Is it %, dollars, thousands, per capita?
Understand scale: What do increments represent?
Look for Specific Data Points
Don't guess: Find exact values or close approximations
Read carefully: 34% is different from 43%
Compare precisely: Which is actually highest/lowest?
Do quick calculations: Calculate differences when needed
Match Claim Scope to Data Scope
Broad claim: Needs data covering full scope (e.g., "all years")
Specific claim: Needs precise data point (e.g., "in 2020")
Comparative claim: Needs data from both/all groups compared
Trend claim: Needs multiple data points showing pattern
Eliminate Wrong Answer Patterns
Wrong numbers: Data doesn't actually show these values
Wrong comparison: Claims opposite of what data shows
Unsupported inference: Goes beyond what data proves
Scope mismatch: Data covers different range than claim
Common Pitfalls & Expert Tips
❌ Misreading units or scale
Graph shows "Population (in thousands)" but you treat it as actual numbers. 50 on the graph = 50,000 people!
❌ Confusing correlation with causation
Data shows two things happening together doesn't mean one caused the other. Stick to what data actually demonstrates.
❌ Reading approximate values as exact
Bar reaches somewhere between 40 and 50, but answer says "exactly 47." Be precise about what you can and can't determine.
❌ Ignoring what the claim actually states
"Species A has more than species B" requires comparing A and B, not just knowing A's value.
✓ Expert Tip: Always read labels first
Spend 10 seconds understanding what the graph shows before looking at questions. Context prevents errors!
✓ Expert Tip: Use your finger to trace
Physically point to data points to avoid reading wrong rows/columns. Simple but prevents careless mistakes!
✓ Expert Tip: Check ALL answer choices
Don't stop at first plausible answer. Verify each against the data to find the MOST accurate choice.
Fully Worked SAT-Style Examples
Passage:
Researchers studied renewable energy adoption across four countries over a 10-year period. The data showed varying rates of growth in solar and wind power capacity.
Table: Renewable Energy Capacity (in Gigawatts)
| Country | 2010 | 2020 |
|---|---|---|
| Country A | 25 | 80 |
| Country B | 15 | 45 |
| Country C | 30 | 95 |
| Country D | 10 | 18 |
Question:
Which choice most effectively uses data from the table to complete the statement?
"Between 2010 and 2020, ______"
Answer Choices:
A) Country D showed the greatest increase in renewable energy capacity.
B) Country C had the highest renewable energy capacity in 2020.
C) Country A more than tripled its renewable energy capacity.
D) All four countries increased their renewable energy capacity.
Correct Answer: C
Why C is correct: Country A went from 25 to 80 GW. 80 ÷ 25 = 3.2, which is more than triple (3×). This is precise and supported by the data.
Why A is wrong: Country C increased by 65 GW (95-30), Country A increased by 55 GW (80-25). Country C had the greatest increase, not D.
Why B is wrong: While true (95 > 80 > 45 > 18), this doesn't address the time frame "between 2010 and 2020" - it only describes the endpoint.
Why D is wrong: Country D went from 10 to 18 (increased), but the statement "All four countries increased" is vague compared to the specific, verifiable claim in C.
Passage:
A study examined student performance across four teaching methods. Students were randomly assigned to groups and tested after 8 weeks.
Graph Description: Bar graph showing "Average Test Score by Teaching Method"
- Method A: 72 points
- Method B: 85 points
- Method C: 78 points
- Method D: 81 points
Question:
Which statement is best supported by the data?
Answer Choices:
A) Method B was approximately 18% more effective than Method A.
B) Method B produced the highest average test scores.
C) Methods B and D together averaged higher scores than Methods A and C together.
D) Method C was the second most effective teaching approach.
Correct Answer: B
Why B is correct: Method B scored 85, which is higher than 81, 78, and 72. This is a straightforward, verifiable fact from the data.
Why A is wrong: While the math works out (85-72 = 13; 13/72 ≈ 18%), the data shows scores, not "effectiveness." We can't conclude one method is more "effective" without knowing other factors.
Why C is wrong: (85+81)/2 = 83; (72+78)/2 = 75. While true, this comparison is oddly specific and not directly shown. B is more direct.
Why D is wrong: Method D (81) was second highest, not C (78). This contradicts the data.
Passage:
Biologists surveyed forest composition in three regions, categorizing trees by type.
Table: Tree Composition by Region (% of total trees)
| Tree Type | Region 1 | Region 2 | Region 3 |
|---|---|---|---|
| Oak | 35% | 20% | 45% |
| Pine | 25% | 55% | 15% |
| Maple | 40% | 25% | 40% |
Question:
Which choice is most strongly supported by the data?
Answer Choices:
A) Pine trees are most common in Region 2.
B) Maple trees make up the same percentage in Regions 1 and 3.
C) Region 3 has more oak trees than Region 1.
D) Oak and maple trees together represent the majority of trees in each region.
Correct Answer: B
Why B is correct: The table shows maple = 40% in both Region 1 and Region 3. This is directly verifiable and precisely stated.
Why A is wrong: While 55% is the highest single value in Region 2, we can't determine if pine is "most common" because percentages don't tell us if there are other tree types not shown.
Why C is wrong: Region 3 has 45% oak vs. Region 1's 35%. But these are PERCENTAGES, not absolute numbers. We don't know if Region 3 has more actual trees.
Why D is wrong: Region 1: 35+40=75% ✓; Region 2: 20+25=45% ✗; Region 3: 45+40=85% ✓. Not true for ALL regions.
Passage:
Economists tracked consumer spending on technology products from 2015 to 2023.
Table: Annual Technology Spending (in billions)
| Year | 2015 | 2017 | 2019 | 2021 | 2023 |
|---|---|---|---|---|---|
| Spending | $120 | $145 | $165 | $185 | $205 |
Question:
Based on the data, which statement about technology spending is most accurate?
Answer Choices:
A) Spending increased by exactly $20 billion every two years.
B) Spending increased consistently between each measured year.
C) By 2023, spending had nearly doubled from 2015 levels.
D) The largest increase occurred between 2019 and 2021.
Correct Answer: B
Why B is correct: Each value is higher than the previous: 120 < 145 < 165 < 185 < 205. This is verifiable and accurately describes the trend.
Why A is wrong: 2015-2017: +$25B; 2017-2019: +$20B; 2019-2021: +$20B; 2021-2023: +$20B. Not "exactly $20 billion every two years."
Why C is wrong: $205/$120 = 1.71, which is not "nearly doubled" (would need to be close to 2.0 or $240).
Why D is wrong: All two-year increases were $20B except 2015-2017 ($25B). The largest increase was actually 2015-2017.
Passage:
Scientists measured water quality indicators in four lakes, testing for pollutant levels.
Table: Pollutant Concentration (parts per million)
| Lake | Nitrogen | Phosphorus |
|---|---|---|
| Lake W | 2.5 | 0.8 |
| Lake X | 1.2 | 1.5 |
| Lake Y | 3.1 | 0.6 |
| Lake Z | 1.8 | 1.2 |
Question:
Which finding is best supported by the data?
Answer Choices:
A) Lake Y has the highest total pollutant concentration.
B) Lake X had higher phosphorus levels than nitrogen levels.
C) Lakes with higher nitrogen levels tend to have lower phosphorus levels.
D) Lake W and Lake Z have nearly identical pollution profiles.
Correct Answer: B
Why B is correct: Lake X: Nitrogen = 1.2, Phosphorus = 1.5. Since 1.5 > 1.2, phosphorus is indeed higher. Directly verifiable.
Why A is wrong: Lake Y total = 3.1+0.6 = 3.7; Lake W total = 2.5+0.8 = 3.3; Lake Z total = 1.8+1.2 = 3.0; Lake X total = 1.2+1.5 = 2.7. Y is highest, but this requires calculation and the question doesn't explicitly ask for totals.
Why C is wrong: Lake Y (high N, low P) and Lake W (moderate N, low P) support this, but Lake X (low N, high P) also supports it. However, this is an inference about a trend, not a direct fact. B is more directly stated.
Why D is wrong: Lake W (2.5, 0.8) and Lake Z (1.8, 1.2) have different values for both pollutants. Not "nearly identical."
Data Interpretation Checklist
Before Answering
✓ Read title and labels
✓ Check units (%, thousands, etc.)
✓ Understand scale
✓ Note what's being measured
When Evaluating Answers
✓ Verify exact numbers
✓ Match claim scope to data
✓ Check all answer choices
✓ Avoid unsupported inferences
Quantitative Evidence: Reading the Numbers Behind Claims
Command of Evidence: Quantitative questions develop data literacy—the ability to extract meaning from numerical information, match quantitative evidence to textual claims, and distinguish supported conclusions from unsupported inferences. The SAT tests this competency because modern life requires interpreting data constantly: reading scientific studies with statistical results, evaluating business reports with financial data, understanding news articles citing surveys and polls, analyzing policy proposals backed by economic projections, and making personal decisions informed by comparative information from nutrition labels to consumer reviews. Strong quantitative reasoning means reading graphs and tables accurately (understanding labels, units, scales), extracting precise information (identifying specific values, calculating differences, recognizing maximums and minimums), recognizing patterns and trends (spotting consistent increases, identifying outliers, comparing groups), and most critically, matching data's scope and precision to claims being supported. When evaluating whether data "most effectively" supports a statement, you practice the same analytical skills researchers use determining which results prove hypotheses, journalists use verifying claims with statistics, policymakers use justifying decisions with evidence, and professionals across fields use connecting assertions to proof. Common pitfalls mirror real-world data misinterpretation: confusing correlation with causation (two variables changing together doesn't mean one caused the other), misreading units or scale (mistaking thousands for millions, percentages for raw numbers), making unsupported inferences (concluding more than data actually shows), and selecting relevant-sounding but imprecise evidence (data relates to the topic but doesn't prove the specific point). The systematic approach—understanding what data displays before analyzing, reading labels and checking units carefully, verifying exact numbers rather than estimating, matching quantitative precision to claim specificity, and eliminating answers contradicting or inadequately supporting claims—represents disciplined data interpretation applicable far beyond standardized testing. Every time you examine a table to verify which country had the highest value, analyze a graph to determine which period showed the greatest increase, or evaluate whether percentages support a claim about proportions, you're developing the quantitative literacy essential for navigating an information landscape where data can illuminate or mislead depending on interpretation accuracy. These skills enable critically evaluating scientific findings, assessing economic claims, understanding statistical arguments, making evidence-based decisions, and participating meaningfully in data-driven discourse across personal, professional, and civic contexts.