36200 Arguments, Evidence and Intuition
The Complete Exam Bible · Spring 202636200 Overview
- University of Technology Sydney
- Spring 2026
- Undergraduate
- 6 credit points
36200 Arguments, Evidence and Intuition is a UTS undergraduate subject worth 6 credit points, offered in Spring 2026. It teaches students to examine persuasion, sources, intuition, probability, data collection, descriptive statistics, association, graphics and big-data claims as connected evidence decisions.
The subject asks a practical question: why should this audience believe this exact claim?
- Assessment evidence Marked tutorials, a Week 8 conceptual quiz and a Week 12 written task are confirmed; weights are not.
- Evidence chain Reconstruct claim, source or design, quantity, rival explanation and bounded conclusion.
- Quantitative check Make denominator, unit, baseline, horizon, subgroup and uncertainty visible.
- Offering Spring 2026 · UTS · 6 credit points.
How 36200 is assessed
| Component | Weight | Format |
|---|---|---|
| Marked tutorial activities | Weight unconfirmed | Best five of nine marked tutorials |
| Mid-session conceptual quiz | Weight unconfirmed | Week 8; content from Weeks 1–7 |
| AT3 written assignment | Weight unconfirmed | Due in Week 12 |
The landed Spring corpus confirms three assessed forms but not their percentage split. Confirm current weights and submission rules in the live UTS subject outline.
What 36200 covers
The path runs from Claims, Persuasion and Source Evaluation through Data Collection, Sampling and Measurement to Data Stories, Big Data and Ethical Communication.
Claims, Persuasion and Source Evaluation
claim · persuasion · source qualityClaims, Persuasion and Source Evaluation asks students to separate a claim's persuasive surface from the authority, relevance and independence of its support.
- claim
- persuasion
- source quality
- separate a claim's persuasive surface from the authority, relevance and independence of its support
- Source credentials cannot repair evidence that is irrelevant to the stated population or causal claim.
Cognitive Bias, Intuition and Critical Thinking
intuition · confirmation bias · critical thinkingCognitive Bias, Intuition and Critical Thinking asks students to use intuition as a hypothesis while checking predictable reasoning errors against evidence.
- intuition
- confirmation bias
- critical thinking
- use intuition as a hypothesis while checking predictable reasoning errors against evidence
- Naming a bias does not prove that a judgement is wrong; the repair requires better comparison or evidence.
Quantitative Literacy, Ratios and Risk
denominator · relative risk · absolute risk differenceQuantitative Literacy, Ratios and Risk asks students to translate quantities, proportions and risk statements into comparable claims with visible denominators.
- denominator
- relative risk
- absolute risk difference
- translate quantities, proportions and risk statements into comparable claims with visible denominators
- A correct calculation does not decide which risk is acceptable or establish causation from association.
Data Collection, Sampling and Measurement
target population · selection bias · measurement validityData Collection, Sampling and Measurement asks students to judge whether a dataset can represent the target population and concept required by the claim.
- target population
- selection bias
- measurement validity
- judge whether a dataset can represent the target population and concept required by the claim
- A very large sample can reduce random error while preserving selection and measurement bias.
Descriptive Statistics, Variation and Graphics
distribution · standard deviation · visual encodingDescriptive Statistics, Variation and Graphics asks students to summarise a distribution without hiding its shape, spread, outliers or comparison scale.
- distribution
- standard deviation
- visual encoding
- summarise a distribution without hiding its shape, spread, outliers or comparison scale
- No single statistic or graphic is universally best; the question and distribution determine the useful summary.
Correlation, Linearity and Simpson's Paradox
correlation · confounding · Simpson's paradoxCorrelation, Linearity and Simpson's Paradox asks students to interpret association with attention to form, subgroup structure and alternative explanations.
- correlation
- confounding
- Simpson's paradox
- interpret association with attention to form, subgroup structure and alternative explanations
- Correlation describes linear association and cannot on its own establish causal direction or mechanism.
Data Stories, Big Data and Ethical Communication
data provenance · algorithmic bias · uncertainty communicationData Stories, Big Data and Ethical Communication asks students to construct a transparent data story that preserves provenance, uncertainty and affected-party consequences.
- data provenance
- algorithmic bias
- uncertainty communication
- construct a transparent data story that preserves provenance, uncertainty and affected-party consequences
- More data and better prediction do not prove that the target, decision rule or distribution of error is legitimate.
A confident speaker, respected institution, large dataset or precise number may be relevant, but none is self-authenticating. The claim's population, period, comparison and degree must be reconstructed before support can be judged.
Persuasion is analysed without assuming that influence is dishonest.
Credibility, reasons, framing and emotion can help an audience understand; they become a problem when persuasive force substitutes for access to method, comparison and uncertainty. Source evaluation therefore tests purpose, expertise, relevance, independence, currency and traceability.
Intuition is treated as a fast hypothesis rather than an enemy of reasoning.
Expertise can produce useful pattern recognition, while familiarity, availability, anchoring and confirmation can create unjustified confidence. The repair is not a bias label but a symmetric search, base rate or comparison that could change the decision.
Quantitative literacy begins with denominator, units, baseline and time. Risk ratios and percentage changes should be reported beside absolute differences.
Probability belongs to a reference class and condition; an event with 80% probability is neither guaranteed nor disproved when the less likely outcome occurs.
Data collection links inference to inclusion. Students distinguish target population, sampling frame and realised sample, then examine selection, nonresponse and measurement.
A huge convenience sample can estimate its own biased pattern very precisely, so size cannot substitute for design.
Descriptive statistics preserve the feature that matters. Mean, median, quantiles and standard deviation answer different questions about centre, spread and tail.
Graphics map values to visual properties, which makes axes, baselines, area, aggregation and definition changes part of the evidence argument.
Correlation is read after the scatterplot. Linearity, range, outliers, subgroups and confounding affect interpretation.
Simpson's paradox demonstrates that aggregate and subgroup comparisons can reverse because group composition changes; the relevant answer depends on the decision, not on a rule that disaggregated is always better.
The final arc treats data storytelling and big data as governed communication. Provenance, transformation, uncertainty and consequence should remain visible.
Predictive accuracy does not prove that a historical target is legitimate, that subgroup errors are acceptable or that affected people have meaningful correction and appeal.
The current landed materials confirm three assessed forms: marked tutorial activities using the best five of nine, a conceptual quiz in Week 8 covering Weeks 1–7, and an AT3 written assignment due in Week 12. Their percentage weights were not recovered and are intentionally not guessed in this resource.
Because weights are unconfirmed, revision should follow dependency rather than imagined marks.
Tutorial work develops classification and explanation; quiz practice strengthens fast concept discrimination; the written task requires an extended evidence story with source, quantity, visual and inference under control.
All practice examples are original. They do not reproduce tutorial questions, the quiz or AT3.
Current weight, deadline, submission, attendance and permitted-material rules remain controlled by the live UTS subject outline and learning system.
A reliable answer route is claim → source or design → quantity or comparison → rival explanation → bounded conclusion. Each arrow must be made visible.
If a calculation is correct but the sample cannot represent the target population, the conclusion must narrow rather than celebrating arithmetic accuracy.
Study with changed cases. Replace the source, denominator, subgroup, measure or chart while holding the rest constant and predict what moves.
This practice separates genuine conceptual understanding from recognition of a familiar example.
The final integrity check asks four things: is the claim exact; does the evidence fit it; is uncertainty communicated at the earned level; and what new observation would change the decision? A data story that answers those questions is both persuasive and accountable.
Worked example · free
Audit one persuasive quantitative claim
- 1Restate the exact claim.
- 1Recover source and design.
- 1Calculate absolute and relative effects.
- 1Check subgroup and alternative explanations.
- 1Communicate a bounded decision.
Key terms
- claim
- A statement presented as true and capable of being supported, qualified or rejected by evidence.
- persuasion
- Communication designed to influence belief or action through reasons, framing, credibility and emotion.
- source quality
- The fitness of a source for a particular claim, considering expertise, method, currency, independence and traceability.
- intuition
- A rapid judgement produced without conscious step-by-step reasoning, often drawing on learned patterns and cues.
- confirmation bias
- The tendency to seek, interpret or remember information in ways that protect an existing belief.
- critical thinking
- Deliberate evaluation of claims, assumptions, evidence and alternatives before accepting a conclusion.
- denominator
- The reference total that gives a count, proportion or rate its scale and meaning.
- relative risk
- A ratio comparing the probability of an outcome in one group with the probability in another.
- absolute risk difference
- The subtraction of two event probabilities, showing the change in percentage-point terms.
- target population
- The complete group of people, objects or events to which a study seeks to generalise.
- selection bias
- Systematic distortion caused when inclusion in the observed sample is related to the outcome or exposure of interest.
36200 FAQ
How is 36200 assessed?
The landed corpus confirms marked tutorial activities, a mid-session conceptual quiz and AT3 written assignment.
What are the assessment weights?
They were not present in the recovered evidence; confirm them in the live UTS subject outline.
Which tutorial marks count?
The best five of nine marked tutorials are identified in the current material.
When is the conceptual quiz?
Week 8, covering material from Weeks 1–7 in the landed schedule.
When is AT3 due?
The current schedule places the written assignment in Week 12; verify the live date and submission settings.
Does correlation prove causation?
No. Form, subgroup structure, confounding, direction and design remain.
Are these official assessment questions?
No. Every supplied problem is original practice.
Where are current operating rules?
Use the live UTS subject outline and Canvas site.
Questions students ask in each 36200 chapter
- What is claim?
- A statement presented as true and capable of being supported, qualified or rejected by evidence. Chapter 01 · Claims, Persuasion and Source Evaluation
- What is intuition?
- A rapid judgement produced without conscious step-by-step reasoning, often drawing on learned patterns and cues. Chapter 02 · Cognitive Bias, Intuition and Critical Thinking
- What is denominator?
- The reference total that gives a count, proportion or rate its scale and meaning. Chapter 03 · Quantitative Literacy, Ratios and Risk
- What is target population?
- The complete group of people, objects or events to which a study seeks to generalise. Chapter 04 · Data Collection, Sampling and Measurement
- What is distribution?
- The pattern of values in a dataset, including centre, spread, shape and unusual observations. Chapter 05 · Descriptive Statistics, Variation and Graphics
- What is correlation?
- A standardised measure of the direction and strength of linear association between two quantitative variables. Chapter 06 · Correlation, Linearity and Simpson's Paradox
- What is data provenance?
- A record of where data came from, how they were transformed and which decisions produced the analysed dataset. Chapter 07 · Data Stories, Big Data and Ethical Communication
How to study for the exam
Use the recurring route claim → source/design → number/comparison → alternative → bounded conclusion, then change one condition and predict the result.
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