Interpreting Information
Decision Making·Lesson 5 of 8·10 min read
Recognising This Question Type
Interpreting Information (II) gives you a passage of text or a data exhibit (chart, table, graph) followed by 5 statements to mark Yes or No. Like Syllogisms, II uses the drag-and-drop format: 5/5 = 2 marks, 4/5 = 1 mark, ≤3/5 = 0.
These make up ~5-6 questions (~14% of DM). Target time: 60-90 seconds per set.
Target time and triage
II is one of the best mark-per-minute trades in DM alongside RA and Probability. Two marks available, technique is mechanical, the trap library is small. Strong opening-pass priority.
The catch: passage length varies enormously. A 3-sentence passage on a familiar topic resolves in 60 seconds. A 10+ sentence dense paragraph on an unfamiliar topic can swallow 3 minutes. Triage on length and topic familiarity before committing.
Two sub-types
- Passage-based II. A block of scientific, medical, or statistical text + 5 yes/no statements. Same technique as VR with one critical difference (below).
- Data-based II. A chart, table, bar graph, or pie chart + 5 yes/no statements. Generally easier and faster than passage-based. You usually get 1-2 data questions per paper and 3-4 passage questions.
How II differs from Verbal Reasoning
This distinction changes the standard of evidence for every statement.
| Verbal Reasoning | Interpreting Information | |
|---|---|---|
| Standard | Only what's explicitly stated | Reasonable inference allowed |
| Passage: "It hasn't rained for six months." Statement: "The region is in drought." | Can't Tell | Yes |
| Passage: "Nutrient-rich water supports phytoplankton growth." Statement: "The water is good for marine food webs." | Can't Tell | Yes |
| Passage: "An investigation has been set up by the sports authority - even though public opinion was in favour of the accused sportsperson." Statement: "The sports authority has taken the claim seriously." | Can't Tell | Yes (acting against public opinion → being taken seriously is a one-step inference) |
| Passage mentions X but not Y. Statement claims something about Y. | Can't Tell | No |
Golden rule
If the statement must be true given the passage (allowing one short, obvious inferential step), it's Yes. If it contradicts the passage, exaggerates it, introduces a claim the passage doesn't address, or could equally be false - No. There is no Can't Tell option.
Passage-based technique
1. Scan the first sentence and triage. Read sentence 1: what topic? How long is the passage?
- ≤6 sentences on a familiar topic → attempt.
- 7-10 sentences → attempt but scan, don't read word-by-word.
- >10 sentences on an unfamiliar topic → flag and skip. Best-guess all 5 to keep the 1-mark partial credit possible.
2. Read the first statement. Don't read all 5 up front. Take them one at a time.
3. Pick a keyword - the irreplaceability test. A good keyword is hard to swap for a synonym: a proper noun, a technical term, a number, a date. "Upwelling" is irreplaceable. "Important" is not. Bad keywords scan to the wrong sentences. The Verbal Reasoning module covers keyword selection and scanning technique in depth - the same approach applies here.
4. Scan the passage for the keyword. Read one or two sentences around it. Do not re-read the whole passage.
5. Verdict. Apply the yes/no rule (below). Move to the next statement.
The yes/no decision rule
Find the matching content. Then ask:
├── Passage directly supports the statement (or one-step inference)
│ → Yes
├── Passage contradicts the statement
│ → No
├── Passage doesn't address this topic / claim
│ → No
├── Statement covers only part of a multi-part claim
│ → No
└── Statement exaggerates the passage
("can reduce" → "eliminates"; "up to 50%" → "by half")
→ NoUnlike VR, silence is "No" not "Can't Tell." If the passage doesn't support it, it's No.
The certainty trap
Watch the statement's modal words. They flip verdicts.
| Word in statement | Risk |
|---|---|
| always, never, all, every, none, must, directly causes | Usually No. Passages rarely justify absolute claims. |
| can, may, some, often, often associated with | Usually safer; check for support. |
| probably, suggests, may not | Hedge words. Need only weak support. |
Example.
A statement reads "The abundance of phytoplankton is directly related to temperature." The passage describes the link as "speculated." Speculation does not justify "directly related." Verdict: No.
Worked example 1: passage-based (upwelling)
Upwelling is a local response of the ocean to surface wind. The warmer, nutrient-depleted surface water is replaced by dense, cooler water that rises to take its place. Speculation about the association of these cooler waters with the enhanced growth of phytoplankton on western tropical coasts has gained ground since chemical analysis of the water revealed a high concentration of dissolved gases along with nutrients like nitrates, phosphates, and silicates. Phytoplankton uses light-harvesting chlorophyll and forms the base of several aquatic food webs.
Mental map after one scan: S1 = upwelling defined. S2 = mechanism (warm replaced by cool). S3 = speculation about phytoplankton + chemical evidence. S4 = phytoplankton's ecological role.
| # | Statement | Keyword | Reasoning | Verdict |
|---|---|---|---|---|
| 1 | Upwelling makes the ocean water more fertile. | upwelling | S2-S3: replaces depleted water with nutrient-rich water → more fertile. One-step inference. | Yes |
| 2 | Phytoplankton is a food source for some marine animal species. | phytoplankton | S4: "forms the base of several aquatic food webs" → it's eaten by something. | Yes |
| 3 | The tropical western coasts may not be a good region for fishing. | western coasts | S3 implies nutrient-rich → supports food webs → likely good for fishing. The statement says NOT good. Contradicted by inference. | No |
| 4 | Warm water is pushed away from the surface of limited marine zones. | warm water | S2: warm surface water is replaced (pushed away) by cool water. Local response → limited zones. | Yes |
| 5 | The abundance of phytoplankton in the regions is directly related to temperature. | directly related | S3 says speculation about an association. "Directly related" is too certain - speculation ≠ direct cause. | No |
Time check
~75 seconds for the set. Statements 3 and 5 are the trap: 3 requires reasonable inference (VR would say Can't Tell, II says No because the passage suggests good fishing); 5 is the certainty trap.
Data-based technique
Data questions are often the easiest marks in the entire DM section. Different sequencing:
1. Read the statement first. Don't study the chart up front.
2. Identify what data point the statement claims. "Individual gift donations rose between 2020 and 2021" → I need individual gifts in 2020 vs 2021. Nothing else.
3. Go straight to the data and check. Eyeball the bars/cells. Verdict in seconds.
4. Watch for inferred causation. If the statement adds "because of an economic downturn" to a falling bar, the data shows the fall but not the cause. Verdict: No.
Worked example 2: data-based (charity donations)
A bar chart shows charity income across three sources (individual gifts, corporate sponsorship, government grants) in 2020 and 2021. Individual gifts rose; corporate sponsorship fell; government grants held steady.
| # | Statement | Verdict | Reasoning |
|---|---|---|---|
| 1 | Donations from individual gifts rose between the two years. | Yes | Chart shows the bar rising. |
| 2 | Donations from corporate sponsorships fell between the two years because of an economic downturn. | No | Bar shows the fall. But "because of an economic downturn" is a causal claim the chart cannot support. Correlation ≠ causation. |
| 3 | Total income from these three sources was lower in 2021 than in 2020. | (depends on chart values) | Just sum the three bars per year and compare. |
Time check
Each statement ~10 seconds. The trap is always Trap 2 below - a causal "because" clause appended to a real data trend.
The 4 traps
Trap 1: Exaggeration
The passage qualifies a claim. The statement removes the qualifier.
| Passage says | Statement says | Verdict |
|---|---|---|
| "can reduce by up to 50%" | "eliminates" | No |
| "for adults and children" | "more effective for children" | No (no comparison made) |
| "speculation about the association" | "directly related" | No |
| "accounts for 8%" | "a rare condition" | Yes (8% is small - fine to infer) |
Trap 2: Correlation vs causation
Any time the statement uses "because," "leads to," "causes," "results in," check whether the passage actually establishes that causal link or just describes co-occurrence. The data shows two things moving together; the statement says one causes the other. Almost always No.
Trap 3: Misreading scales and axes
For data questions:
- What does the y-axis measure? (absolute numbers vs percentages vs rates per 1,000)
- Where does the y-axis start? (a chart starting at 30 makes 40→60 look enormous)
- Are the intervals even? (a time series with missing years can mislead)
Trap 4: Going beyond the passage
The passage doesn't mention X. The statement makes a claim about X. Default to No - silence is not support.
Numerical inference
II passages often embed numbers that you need to calculate with.
"It takes approximately 3.5 hours to add layers of egg white and shell. The complete egg weighs about 58 g, of which 18 g is the yolk."
Statement: "The layers other than the yolk weigh about 2.23 times more than the yolk."
58 − 18 = 40 g (non-yolk). 40 / 18 = 2.22. Statement says "about 2.23" → Yes.
Rule: "About" and "approximately" give you rounding room. Within a few percent is fine.
When to flag and skip
Scan sentence 1 of the passage.
|
├── ≤6 sentences, familiar topic
│ → Attempt. 60-75s target.
│
├── 7-10 sentences OR moderately technical
│ → Attempt. Scan-don't-read. 90s target.
│
└── >10 sentences AND opaque topic
→ Flag. Best-guess all 5 statements (you might still get 1 mark
partial credit). Return only if banked time after Venn/Prob.Data-based II should almost never be skipped.
Common Mistakes
- Applying VR's "Can't Tell" threshold. In II, reasonable one-step inference is allowed. "Auto-immune" → "the body's own immune system attacks itself" is valid in II but Can't Tell in VR. Adjust your threshold.
- Accepting "could be true." Could ≠ must. If there's a plausible reading where the statement is false, it's No.
- Reading the whole passage before looking at statements. Wastes time. Map the passage (10s scan) then test each statement against its keyword.
- Missing the certainty trap. "Directly," "must," "always," "never" almost always flip the answer to No.
- Importing causation from correlation. A "because" clause attached to a real trend is usually a No.
Summary
| Element | Detail |
|---|---|
| Format | Passage or data + 5 statements, 2 marks (drag-and-drop: 5=2, 4=1, ≤3=0) |
| Sub-types | Passage-based (3-4 per paper) and data-based (1-2 per paper) |
| Passage technique | Triage on length → first statement → irreplaceable keyword → scan → verdict |
| Data technique | Read statement → go straight to the chart → check that one data point |
| Yes | Passage supports it (one-step inference allowed) |
| No | Contradicts, exaggerates, silent on it, only partially covers, or adds unjustified causation |
| Time target | 60-90 seconds per set |
| Key traps | Exaggeration, correlation→causation, scale misreads, certainty words |
Underlying Skills
II questions test three skills:
- D1: Scientific / Technical Passage Inference - close reading of dense text, distinguishing what's stated from what's inferred. The keyword-scan technique avoids re-reading.
- D2: Numerical / Quantitative Passage Inference - extracting numbers from text and verifying claims arithmetically. Arithmetic must be exact unless "about" appears.
- D3: Graphical / Chart Data Interpretation - reading bar charts, tables, line graphs. Watch axes and units; the data shows trends, not causes.