Cambridge International AS and A Level Thinking Skills Exam Set

Understand information

Ability to interpret information given as text

Example of Understand information

Using a timetable and a map together to choose the best bus.

Extract relevant information

Selecting only the information needed to solve the problem.

Example of Extract relevant information

Ignoring ages when only total cost matters.

Extract data from related data sets

Combining information from multiple sources.

Example of Extract data from related data sets

Using hours worked and pay rate from two tables to find weekly pay.

Simple models

A rule showing how inputs produce outputs.

Example of Simple models

Taxi fare = 3 + 1.5 × distance.

Necessary condition

Something that must be true for a result to occur.

Example of Necessary condition

A ticket is necessary to board a plane.

Sufficient condition

Something that guarantees the result by itself.

Example of Sufficient condition

Scoring 100% is sufficient to pass.

Deduce information from processed data

Using summaries or graphs to infer original data.

Example of Deduce information from processed data

High average score with low variation means most students scored high.

Use information appropriately

Choosing and performing the correct calculations.

Example of Use information appropriately

Multiplying price × quantity to find total cost.

Apply a model

Substituting real values into a rule or formula.

Example of Apply a model

Using cost = 3 + 1.5 × 10 to find taxi fare.

Search through possible solutions

Checking all options to find those meeting every condition.

Example of Search through possible solutions

Selecting students over 16 with perfect attendance.

Identify unmet criteria

Spotting which rule a proposed solution breaks.

Example of Identify unmet criteria

A schedule breaks the rule “no lessons after 5 pm.”

Make appropriate deductions

Drawing new conclusions from given information.

Example of Make appropriate deductions

If A > B and B > C

Recognise alternative representations

Seeing that different formats show the same data.

Example of Recognise alternative representations

A bar chart and pie chart showing the same proportions.

Identify features of a model from representations

Interpreting graphs or tables to understand model behaviour.

Example of Identify features of a model from representations

Gradient of a distance–time graph represents speed.

Explain trends

Giving plausible reasons for patterns in data.

Example of Explain trends

Sales rise in December due to holiday shopping.

Fit a model to information

Adjusting a formula so it matches data.

Example of Fit a model to information

Deducing fixed fee plus per‑km rate from taxi prices.

Impact of a change

Considering how a scenario change affects a solution.

Example of Impact of a change

Road closure increases travel time so schedule must be adjusted.

Identify features to include in a model

Choosing which real‑world factors must be represented.

Example of Identify features to include in a model

Including rush‑hour traffic in a travel‑time model.

Adjust a model

Modifying a model to better match reality.

Example of Adjust a model

Adding a peak‑time surcharge to a fare model.

Credibility of evidence

How believable evidence is.

Example of Credibility of evidence

A peer‑reviewed study is more credible than an anonymous blog.

Reliability

How trustworthy the source is.

Example of Reliability

A trained observer is more reliable than someone who heard a rumour.

Plausibility

Whether the claim itself seems likely.

Example of Plausibility

“People need sleep” is plausible.

Corroboration

Two sources support the same claim.

Example of Corroboration

Two surveys show similar results.

Consistency

Evidence does not contradict other evidence.

Example of Consistency

Two witnesses give compatible timelines.

Representativeness

Whether a sample reflects the population.

Example of Representativeness

Surveying only teenagers does not represent all adults.

Assess presentation of data

Checking for misleading graphs or tables.

Example of Assess presentation of data

A truncated y‑axis exaggerates differences.

Assess explanation

Judging whether an explanation fits all evidence.

Example of Assess explanation

Ignoring half the data makes an explanation weak.

Assess inference

Checking whether a conclusion logically follows.

Example of Assess inference

“Some students cheat → all students cheat” is invalid.

Suggest explanation

Proposing a plausible cause.

Example of Suggest explanation

Sales drop due to a new competitor.

Suggest inference

Drawing a reasonable conclusion from evidence.

Example of Suggest inference

90% satisfaction suggests the product is well‑received.

Form a judgement

Combining multiple sources to reach a conclusion.

Example of Form a judgement

Reading several studies before deciding if a policy works.

Recognise an argument

Identifying when reasons support a conclusion.

Example of Recognise an argument

“Ban cars because they cause pollution.”

Main conclusion

The main claim being argued for.

Example of Main conclusion

“Therefore

Intermediate conclusion

A conclusion that also supports another conclusion.

Example of Intermediate conclusion

“Higher wages reduce turnover; reduced turnover saves money.”

Reason

A statement supporting a conclusion.

Example of Reason

“Because it reduces accidents.”

Counter‑assertion

A claim opposing the main argument.

Example of Counter‑assertion

“Some say raising wages increases unemployment.”

Counter‑argument

A reasoned objection to a claim.

Example of Counter‑argument

“Evidence shows employment did not fall.”

Example (argument element)

A specific case supporting a claim.

Example of Example (argument element)

“In City X

Evidence

Data

Example of Evidence

“A 2023 study found a 10% wage rise increased productivity.”

Unstated assumption

A hidden step required for the argument to work.

Example of Unstated assumption

“Close the park because it’s dangerous” assumes closing reduces danger.

Equivocation

Using a word with two meanings as if it had one.

Example of Equivocation

“Feathers are light so feathers can’t be dark.”

Conflation

Treating two different concepts as identical.

Example of Conflation

Assuming legal equals moral.

Circular argument

Using the conclusion as a reason.

Example of Circular argument

“He’s honest because he tells the truth.”

Begging the question

Assuming what must be proved.

Example of Begging the question

“Games are harmful because they’re bad.”

Invalid deduction

Incorrect logical reasoning.

Example of Invalid deduction

“The ground is wet so it must have rained.”

Causal flaw

Assuming correlation equals causation.

Example of Causal flaw

Ice cream sales cause drowning.

Rash generalisation

Drawing a conclusion from too little evidence.

Example of Rash generalisation

“Two rude teens means all teens are rude.”

Sweeping generalisation

Applying a rule without allowing exceptions.

Example of Sweeping generalisation

“Exercise is good so everyone must run daily.”

False dichotomy

Presenting only two options when more exist.

Example of False dichotomy

“Support this law or hate safety.”

Confusing necessary and sufficient

Mixing up what is required vs what is enough.

Example of Confusing necessary and sufficient

“A degree is required so a degree guarantees the job.”