SAT: Quantitative Evidence Cheat Sheet

The core ideas of SAT: Quantitative Evidence distilled into a single, scannable reference — perfect for review or quick lookup.

PiqCue — piqcue.com/sat-quantitative-evidence/cheatsheet

Quick Reference

Claim-Data Alignment

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.

Reading Axis Labels and Units

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.

Identifying Trends in Line Graphs

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.

Comparing Categories in Bar Charts

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.

Reading Data Tables

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.

Supports vs. Undermines

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.

Relevant vs. Irrelevant Data

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.

Correlation vs. Causation

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.

Sample Size and Generalizability

Assessing whether the data comes from a sample large or representative enough to justify the claim. The SAT may present data from a small or unrepresentative group and ask whether a broad conclusion is warranted.

Data Visualization Types

Recognizing the purpose of different chart types: bar charts compare categories, line graphs show change over time, pie charts show proportions of a whole, scatter plots show relationships between two variables, and tables present precise values.

Key Terms at a Glance

Bar Chart:A chart that uses rectangular bars of varying lengths to compare values across categories. Bars can be vertical or horizontal.

Line Graph:A chart that uses points connected by lines to show how a variable changes over time. Useful for identifying trends, plateaus, and fluctuations.

Scatter Plot:A chart that plots individual data points on two axes to show the relationship between two variables. Often includes a trend line.

Pie Chart:A circular chart divided into slices that represent proportions of a whole. Each slice shows a category's percentage contribution.

Data Table:A grid of rows and columns that presents precise numerical values organized by category and variable.

Trend:The general direction of change in data over time: upward (increasing), downward (decreasing), or flat (stable).

Correlation:A statistical relationship between two variables that change together. Positive correlation means both increase; negative correlation means one increases as the other decreases.

Selection Bias:A distortion that occurs when the participants in a study are not representative of the population, often because they self-selected into the study.

Outlier:A data point that is significantly different from the other values in a dataset. Outliers may indicate errors, unusual cases, or external factors.

Confounding Variable:A third variable that influences both the independent and dependent variables, creating a misleading appearance of a direct relationship between them.

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