26134 · Responsible Evidence-based Decisions
The Complete Exam Bible · Spring 2026Responsible Evidence-Based Decisions
26134 Responsible Evidence-Based Decisions is the University of Technology Sydney's first-year, core business-statistics subject (formerly "Business Statistics") in the UTS Business School, and it is worth 6 credit points toward your Weighted Average Mark (WAM). This subject runs across five modules over the session. Module 1 is descriptive statistics and data communication (Weeks 1-2): data types, measures of central tendency and spread, distribution shape, choosing honest charts. Weeks 3-5 bridge to inference through probability, the binomial distribution and the normal distribution. Module 2 is inferential statistics (Weeks 6-7): sampling distributions, the Central Limit Theorem and confidence intervals. Module 3 is hypothesis testing (Weeks 8-9): one-sample tests, then two-sample, paired, ANOVA and F-tests, routed with a decision tree. Module 4 is simple linear regression (Week 10), and Module 5 is the ethical use of data (Week 11). It is a method-and-interpretation subject: nearly every item asks you to pull a value from a scenario or table, apply one formula or table lookup, and state a decision or a one-line responsible interpretation. Assessment runs through Canvas as six online quizzes (best 4 of 6 count, 20%), a group data-analysis assignment (30% — confirm the current-session weight on the UTS subject outline) and a 120-minute restricted open-book final examination worth 50%. The final is invigilated online through ProctorU: 25 equally-weighted questions presented all at once, with paper notes, a calculator of any type and offline Excel permitted (no internet, no cloud notes), and no negative marking applies. UTS grades on the HD / D / C / P scheme, and the UTS academic-integrity policy governs every component.
What 26134 covers
26134 runs across five modules over the UTS session, building from describing data to drawing inferences from it. You start with descriptive statistics and data communication, move through probability and the binomial and normal distributions, then into sampling distributions, confidence intervals, hypothesis testing and regression, and finish with the ethical use of data. It is assessed by six online quizzes (best four count, 20%), a group assignment (30%) and a 120-minute restricted open-book final exam worth 50%.
How 26134 is assessed
| Component | Weight | Format |
|---|---|---|
| Online quizzes (best 4 of 6 count) | 20% | Individual online Canvas quizzes released across the session (six quizzes; best four count) |
| Group assignment | 30% | Group data-analysis project submitted via Canvas (weight is the standard structure — confirm current-session weight on the UTS subject outline) |
| Final examination | 50% | 120-minute restricted open-book online exam (ProctorU-invigilated); 25 equally-weighted questions presented all at once; calculator and offline notes/Excel permitted, no internet |
One-sample t-test for a mean (population SD unknown)
- +1State the hypotheses and pick the statistic. H₀: μ = 40 versus H₁: μ ≠ 40 (two-tailed). The population SD σ is unknown and n is small, so use a one-sample t-test with df = n − 1 = 15 and read the t-table.
- +1Compute the standard error of the mean: SE = s / √n = 9.0 / √16 = 9.0 / 4 = 2.25 hours.
- +1Compute the test statistic: t = (x̄ − μ₀) / SE = (44.5 − 40) / 2.25 = 4.5 / 2.25 = 2.00 on 15 degrees of freedom.
- +1Decide against the critical value. For a two-tailed test at α = 0.05, t₀.₀₂₅,₁₅ = 2.131 from the t-table. Since |t| = 2.00 < 2.131, the statistic falls outside the rejection region, so fail to reject H₀ (p-value > 0.05).
Key terms
- Descriptive vs inferential statistics
- Descriptive statistics summarise a dataset you have (mean, spread, shape, charts); inferential statistics use a sample to draw conclusions about a population you cannot fully observe (estimation and hypothesis testing). 26134 spends Module 1 on the first and Modules 2-4 on the second.
- Standard error (SE)
- The standard deviation of a sample statistic across all possible samples. For the sample mean, SE = σ/√n (or s/√n when σ is unknown). It shrinks as n grows, which is why larger samples give tighter confidence intervals and more powerful tests.
- Confidence interval
- An interval estimate point estimate ± (critical value) × SE that is built by a procedure which captures the true parameter in a proportion 1 − α of repeated samples. A 95% CI does NOT mean a 95% probability the parameter lies in this one interval — the parameter is fixed and the interval is random.
- Test statistic
- A standardised distance of the sample estimate from the value assumed under H₀ (a z, t, χ² or F). You compare it to a critical value from the matching table, or convert it to a p-value: reject H₀ if the statistic is in the rejection region, or equivalently if p ≤ α.
- Type I vs Type II error
- A Type I error rejects a true null hypothesis (probability α, the significance level); a Type II error fails to reject a false null (probability β). Power = 1 − β. Lowering α to guard against false positives raises β unless you also raise n.
- Restricted open-book exam
- The 26134 final format: paper notes, textbook, a calculator of any type and offline Excel/Word/PDF notes are permitted, but the internet and any cloud-stored notes are not. It is ProctorU-invigilated, 120 minutes, 25 equally-weighted questions, with no negative marking.
26134 FAQ
Is 26134 hard?
It is broad rather than deeply mathematical. 26134 spans five modules — descriptive statistics and communication, probability and distributions, sampling and confidence intervals, hypothesis testing, regression and data ethics — so the real challenge is keeping a lot of small methods straight and knowing which one a question wants, not any single hard idea. Because the restricted open-book exam supplies the Z, t, chi-square and F tables, the marks go to setting up the right formula with correct units, reading the right table, and stating a one-line interpretation. Students who work one example per method each week, rather than cramming the revision week before the formal exam period, tend to find it manageable; steady work also protects your WAM. First-year students often meet it as a core subject with no statistics prerequisite.
Can AI help me with 26134?
Yes, as a step-by-step study aid. Sia is an AI tutor trained on how 26134 is actually taught and assessed at University of Technology Sydney: it can walk you through standardising a Z-score, building a confidence interval, choosing and running the right hypothesis test from the decision tree, fitting a least-squares line or applying the Five Safes framework, one line at a time, and it checks your reasoning as you go. Bring your own tutorial or practice question and ask Sia to explain each step. It does not do graded assessment for you — quizzes, the group assignment and the exam are your own work — and the University of Technology Sydney academic-integrity policy still applies. Use it to understand the method, not to produce work you submit.
Where can I find past exam papers / practice for 26134?
Start on Canvas, where the subject posts its exam-preparation material — a full practice exam (the ProctorU practice run), sample questions, and the five statistical tables (Z, t, chi-square, F, binomial) that the exam provides. The UTS Library also holds a past-exam-paper collection; note that older papers may still be labelled "Business Statistics", the subject's former name. Your tutorial questions and their worked solutions are the closest match to the exam's compute-then-interpret style. This guide also includes a re-authored practice exam that mirrors the paper's shape — descriptive stats, a probability item, a distribution lookup, a confidence interval, a hypothesis test, a regression read and an ethics scenario — with fresh numbers, and you can ask Sia to generate extra practice in the same style and explain each step. Confirm what is officially provided on Canvas.
What are the 26134 assessment rules and is there a hurdle?
26134 is assessed by six online Canvas quizzes with the best four counting (20% total), a group data-analysis assignment (30% — the standard structure; confirm the current-session weight on the UTS subject outline), and a 120-minute restricted open-book final examination worth 50%. No pass hurdle or minimum-mark requirement is stated in the subject materials, and UTS applies no negative marking on the exam, but you should confirm the exact weights, any hurdle and the permitted-materials list on your current Canvas Assessments page and the UTS subject outline. The final is invigilated online through ProctorU (a Guardian browser); an on-campus sitting can be requested. Grades follow the UTS HD / D / C / P scheme, and the UTS academic-integrity policy governs every component.
What is on the 26134 final exam?
One 120-minute restricted open-book paper worth 50% of the subject, delivered online via ProctorU. It has 25 equally-weighted questions presented all at once (you can move back and forth), each a compact compute-then-interpret item spread across all five modules — including Week 2 data communication and Week 11 data ethics, which students sometimes forget are examinable. You are given the Z, t, chi-square, F and binomial tables and may use a calculator and offline Excel, so the emphasis is applying methods, not memorising formulas. With no negative marking, answer every question. The exam sits in the University of Technology Sydney formal examination period for the Spring session (around November 2026) — confirm the exact date, time and format on Canvas and the UTS exam timetable.
How to study for the exam
Treat 26134 as a toolbox of about a dozen small methods rather than one long reading, and rehearse one worked example per method every week rather than cramming the revision week before the formal exam period. Build a single decision tree that routes any inference question: one sample or two? A mean, a variance, or a difference? Is σ known (→ Z) or unknown (→ t)? Paired or independent? Two groups (t / F) or more (ANOVA)? Then drill the table lookups until they are automatic — standardising with the Z-table, t critical values for a CI or a t-test, chi-square for a variance, F for ANOVA and equal-variance tests. Because the restricted open-book exam supplies all five tables and allows a calculator and offline Excel, practise applying each formula with correct units and a sanity check, not memorising it, and pre-organise your printed notes so you can find the right table and formula fast under the 120-minute clock. Do not neglect the two modules students under-prepare: Week 2 data communication (truncated axes, pie-chart pitfalls, percentage points vs percent change) and Week 11 data ethics (the four principles, consequentialist vs deontological reasoning, the Five Safes) are both examinable short-answer territory. With 25 equally-weighted questions and no negative marking, keep pace at roughly one question every four to five minutes, bank the easy computes first, and attempt every question. When a step will not click, ask Sia to explain that single step a different way and set you a fresh practice question in the same style; it teaches the method and checks your reasoning, and it never substitutes for your own graded work. Confirm the exam date, permitted materials and any assessment changes on Canvas and the UTS subject outline.
Your AI Statistics tutor for 26134
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