Interpreting Information

Interpreting Information (II) gives you a dense passage, chart, or table and 5 statements to mark Yes or No. Like Syllogisms, these use the drag-and-drop format and are worth 2 marks.

The stimuli are typically scientific, medical, or statistical - longer and more detailed than Syllogism premises. This question type feels the most like Verbal Reasoning, but the standard of evidence is different.

These make up ~5-6 questions (~14.3% of DM). Target time: 60-90 seconds per set.

This is a distinction you need to get right, because it changes the standard of evidence for every statement.

The golden rule: If the statement must be true given the information, it's Yes. If it could go either way - even slightly - it's No. You've got more room for inference than VR, but "could be true" still isn't enough.

Step 1: Skim the stimulus (10-15s). Read the first sentence for the topic. Scan the rest, noting what each sentence or data point covers. Don't memorise - just build a mental map. "S1 = definition. S2 = historical problem. S3 = new technology. S4 = statistics."

Step 2: Read the statement. What does it CLAIM? Break it down: what topic does it address? What specific claim does it make? Does it use absolute language ("all", "none", "always") or qualified language ("some", "can")?

Step 3: Find the matching content. Use non-interchangeable keywords - proper nouns, technical terms, numbers, percentages - to locate the relevant sentence(s). These can't be swapped for synonyms, so they're reliable anchors.

Step 4: Compare claim to source. Does the passage support EXACTLY what the statement says? Check for: exaggeration (qualified to absolute), unsupported comparison, going beyond, contradiction.

Step 5: Verdict. Must be true - Yes. Could go either way, contradicts, or goes beyond - No.

Unlike VR, there's no "Can't Tell" option. If the passage doesn't support it, it's No - whether the passage contradicts it or simply doesn't address it.

Passage:

"Type 1 diabetes is an auto-immune condition that cannot be prevented and accounts for about 8% of all diabetes cases. Historically, the sufferers were condemned to countless blood sugar tests and worry about an attack brought on by dangerously low blood sugar while they slept. However, the latest wearable tech now provides real-time continuous glucose monitoring for adults and children and it can reduce the need for often painful finger-prick testing by up to 50%."

First, build your mental map: S1 = definition + stats. S2 = historical problem (blood sugar tests, nighttime danger). S3 = new tech solution + stats.

Time check: The passage is 3 sentences. Mapping it takes ~10 seconds. Each statement takes ~10-15 seconds once you know where to look. Total: ~70 seconds.

Stimulus: A table showing patient outcomes across three hospitals.

Time check: Quick arithmetic for each row. The tricky one is (c) - it smuggles in a causal claim that the data can't support. ~60 seconds total.

Trap 1: Exaggeration

The passage says something qualified. The statement removes the qualification.

Trap 2: Correlation vs. Causation

The passage shows two things happening together. The statement says one causes the other. Unless the passage explicitly states causation, the causal claim doesn't follow.

Passage: "Countries with higher tea consumption have lower rates of heart disease."
Statement: "Tea consumption prevents heart disease."
Answer: No. The passage shows correlation, not causation. Other factors (diet, healthcare access) could explain it.

This is one of the most reliable traps in II. Any time a statement uses "because," "leads to," "causes," or "results in," check whether the passage actually establishes that causal link or just describes a pattern.

Trap 3: Misreading Scales and Axes

When the stimulus is a chart or graph:

• Check what the y-axis measures (absolute numbers? percentages? rates per 1000?)
• Check the scale intervals (is the gap between gridlines 10 or 100?)
• Check whether the x-axis is evenly spaced (time series with missing years can mislead)

A statement might say "doubled between 2010 and 2020" when the chart shows it went from 40 to 60 - visually looks like a big jump if the y-axis starts at 30, but it's a 50% increase, not 100%.

Trap 4: True But Doesn't Answer the Question

A statement can be factually correct based on the passage but not what the question is asking. This is rare in II but it happens: the statement matches the passage topic but answers a different question than the one posed.

II passages often embed numbers you need to calculate with:

Passage: "…the complete egg weighs about 58g, of which 18g is the yolk."
Statement: "The layers other than the yolk weigh about 2.23 times more than the yolk."

Calculation: 58 − 18 = 40g (non-yolk). 40 / 18 = 2.22…
"About 2.23 times" → Yes (close enough).

Rule: "About" or "approximately" gives you rounding room. If your calculation is within a few percent of the statement's claim, it follows.

II questions test three skills from the DM taxonomy:

• D1: Scientific / Technical Passage Inference - close reading of dense text, distinguishing what's stated from what's merely implied. The key is not importing unstated conclusions.
• D2: Numerical / Quantitative Passage Inference - extracting numbers from text and performing calculations to verify claims. The trap is arithmetic errors or mixing up percentages and ratios.
• D3: Graphical / Chart Data Interpretation - reading and interpreting visual data. Check axes, scales, and units before drawing conclusions.

The Claim-Check technique applies to all three. The difference is where you find the matching content: in text (D1), in embedded numbers (D2), or in charts/tables (D3).

II passages vary enormously in difficulty. Some are 3 clear sentences about a medical topic. Others are dense paragraphs about obscure scientific studies.

Run through this every time:

1. What does the statement claim?
2. Where in the passage/data is this topic covered?
3. Does the passage support the claim exactly, or does the statement go further?
4. Does the statement add a causal link the passage doesn't make?
5. Does the statement strengthen, weaken, or match the passage's language?

If you can answer "the passage says this, and the statement says the same thing or a valid one-step inference" - it's Yes. Anything else is No.

1. Applying VR logic - In VR, "drought" from "climatic fluctuation" would be Can't Tell. In DM, a reasonable one-step inference is valid. Adjust your threshold.
2. Accepting "could be true" - "Could be true" isn't "must be true." If there's any reasonable scenario where the statement is false given the passage, it's No.
3. Missing the exaggeration - "Up to 50% reduction" isn't the same as "50% reduction." "Can improve" isn't "will improve." Watch for strengthened language.
4. Assuming causation from correlation - The passage says two things co-occur. The statement says one causes the other. That's a leap the data doesn't support.
5. Reading the whole passage before looking at statements - This wastes time. Build your map first, then check each statement against the relevant section.

Next lesson: 2.5 Venn Diagrams