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The Decision Making subtest includes questions that require you to extract, compare, and reason about data presented in tables and graphs. Unlike the Quantitative Reasoning subtest (which tests calculation skills), DM data interpretation focuses on logical conclusions — what the data supports, what it does not, and what inferences are valid.
Data interpretation questions in DM typically present:
You are then asked to evaluate one or more statements about the data. These may be in standard MCQ format (pick one correct answer from four) or Yes/No format (decide if each of several statements follows from the data).
| Feature | Decision Making | Quantitative Reasoning |
|---|---|---|
| Focus | Logical validity of conclusions | Numerical calculation |
| Calculation | Minimal — estimation is often sufficient | Precise calculation expected |
| Question type | "Does this conclusion follow?" | "What is the value of...?" |
| Time per question | ~64 seconds | ~40 seconds |
This sounds obvious, but misreading data is the single most common error in DM data interpretation questions. Common causes of misreading:
Prevention Strategy: Before looking at any question, spend 5-10 seconds understanding the structure of the data. Read the title, column/row headers, axis labels, and any legends or footnotes.
Trend questions ask you to determine whether data is increasing, decreasing, stable, or fluctuating:
| Trend Type | What to Look For |
|---|---|
| Increasing | Each successive value is larger |
| Decreasing | Each successive value is smaller |
| Stable | Values remain approximately the same |
| Fluctuating | Values go up and down without a clear direction |
| Accelerating increase | Not just increasing, but increasing at a faster rate |
Warning: A single anomalous data point does not negate an overall trend. "Sales generally increased from 2018 to 2023" can be true even if there was a small dip in 2020.
When comparing data points, be precise about what you are comparing:
A frequent DM trap is presenting two trends that move together and asking whether one causes the other. Just because two variables correlate does not mean one causes the other.
Example: "As the number of hospitals in a region increased, life expectancy also increased. Therefore, building more hospitals causes higher life expectancy."
This does not follow. Both variables might be driven by a third factor (e.g., economic development).
Scenario:
The table below shows the number of applicants to five medical schools in two consecutive years:
| Medical School | Year 1 Applicants | Year 2 Applicants |
|---|---|---|
| Alpha | 3,200 | 3,450 |
| Beta | 2,800 | 2,650 |
| Gamma | 4,100 | 4,100 |
| Delta | 1,900 | 2,200 |
| Epsilon | 3,500 | 3,400 |
Statement to evaluate: "All medical schools with more than 3,000 applicants in Year 1 experienced an increase in Year 2."
Identify medical schools with more than 3,000 applicants in Year 1:
The statement does NOT follow. Gamma stayed the same and Epsilon decreased.
Key Technique: For "All..." statements, you only need one counterexample to prove the statement does not follow.
Scenario:
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