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Session Length
~17 min
Adaptive Checks
15 questions
Transfer Probes
15
The SAT Reading and Writing section increasingly tests students' ability to interpret quantitative information presented alongside text. These questions pair a written claim or argument with a data visualization -- a bar chart, line graph, table, or infographic -- and ask students to determine which data point supports, undermines, or is most relevant to the claim. Success requires not just reading comprehension but also data literacy: the ability to extract specific values from visual displays and connect them to textual arguments.
Quantitative evidence questions mirror the kind of reasoning required in college courses and professional settings, where reports, research papers, and policy briefs routinely combine prose with data. A biology student must interpret experimental results presented in tables. A business analyst must determine whether sales data supports a marketing claim. A policy researcher must evaluate whether survey results justify a proposed intervention. The SAT tests this cross-format reasoning because it is central to academic success across disciplines.
These questions are not math problems. They do not require calculation, formulas, or statistical knowledge. Instead, they require careful reading of axis labels, titles, units, and data trends, combined with the ability to match specific data points to specific textual claims. The most common errors involve misreading the claim, confusing correlation with causation, selecting data that is related but does not directly address the claim, or failing to notice the specific conditions (time period, subgroup, variable) the claim specifies.
One step at a time.
The skill of matching a specific textual claim to the specific data point that supports or contradicts it. On the SAT, the correct answer is the data that directly addresses the claim -- not data that is merely related to the topic.
Example: If the claim is 'Species X population declined between 2010 and 2015,' the supporting data must show Species X (not Y), in the correct time range (not 2005-2010), and a decline (not stability).
Understanding what each axis of a chart or graph represents, including the variable name, scale, and units of measurement. Misreading an axis is one of the most common errors on quantitative evidence questions.
Example: A bar chart with 'Revenue (millions of dollars)' on the y-axis and 'Quarter' on the x-axis shows revenue per quarter. A student who misses 'millions' might interpret $2.5 as $2.50 instead of $2,500,000.
Recognizing whether a line graph shows an increase, decrease, plateau, or fluctuation over time. The SAT tests whether students can describe trends accurately and connect them to textual claims.
Example: A line graph showing temperature rising from 1980 to 2000 and then leveling off from 2000 to 2020 supports a claim about warming followed by stabilization, not a claim about continuous warming.
Extracting and comparing values across different categories or groups in a bar chart. The SAT may ask which group has the highest or lowest value, or how two groups compare.
Example: A grouped bar chart comparing test scores by school shows School A at 78 and School B at 85. This supports 'School B outperformed School A' but does not support 'School A had the lowest scores' unless all other schools are also shown.
Locating specific values in rows and columns of a data table. The SAT may present tables with multiple variables and ask students to find the value at the intersection of a specific row and column.
Example: In a table showing crop yields by region and year, finding 'Region C, 2018' requires locating the correct row (Region C) and the correct column (2018), then reading the value at their intersection.
Distinguishing between data that strengthens a claim (supports) and data that weakens or contradicts it (undermines). The SAT may ask for either direction, and confusing the two is a common error.
Example: Claim: 'Exercise reduces stress.' Data showing that exercisers report lower stress supports the claim. Data showing that exercisers report the same stress as non-exercisers undermines it.
Determining whether a data point actually addresses the specific claim being made. Irrelevant data may be about the right topic but the wrong variable, time period, or population.
Example: Claim: 'Rainfall increased in the Midwest in 2019.' A data point showing Midwest temperature in 2019 is about the right region and time but the wrong variable -- it is irrelevant to a rainfall claim.
Understanding that two variables changing together (correlation) does not prove that one causes the other. The SAT may test whether a claim goes beyond what the data actually shows.
Example: A chart showing that ice cream sales and drowning deaths both rise in summer shows correlation, not causation. The data does not support the claim 'ice cream causes drowning' -- a third variable (hot weather) explains both.
More terms are available in the glossary.
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