STATS100: pass the exams, not just read the notes
Your complete guide to University of Auckland's concepts in statistics course. See where the marks are, work real practice questions, and study with an AI tutor that knows STATS100.
Sia generates STATS100 practice questions, walks through data sources and making predictions step by step, and quizzes you on the material the exam weights most heavily.
Worked example
A café claims 70% of its customers prefer oat milk. In a random sample of 100 customers, 61 prefer oat milk. A simulation of 1,000 samples of size 100 from a population where 70% prefer oat milk finds that only 25 of the 1,000 samples had 61 or fewer oat-milk preferences. What is the best conclusion?
Identify the chance model: a population where 70% prefer oat milk, sampled 100 at a time.
Interpret it correctly: a result this low would be surprising if the claim were true, so the data provide evidence against the 70% claim.
Rule out the traps. The p-value is not the probability the claim is false (option B); the sample gives an estimate of 61% with uncertainty, not the true value (option C); a sample of 100 is informative here (option D).
The trap: Turning the p-value into a probability about the claim. 2.5% is how often the chance model produces data this extreme, not how likely the café is wrong. classic slip!
One exam decides 50% of your grade. At least 45% required in this component. This whole page is built around that.
Overview
What STATS100 is, and where it sits
STATS 100 is the University of Auckland's foundation statistics course, positioned by the catalogue for students with a limited background in statistics or mathematics. Its stated goal is to build confidence and interest: conceptual thinking through real data, computer simulations, hands-on activities and projects, with data literacy across disciplines rather than formula manipulation.
The catalogue publishes four topics — making predictions, conducting tests, building models, informing decisions — and eight learning outcomes that span manipulating data from a range of sources, choosing software to analyse it, reasoning critically with data and models, using mathematical representations, writing summaries that communicate uncertainty, designing investigations, and the ethics and social consequences of data-based decisions.
Assessment is a 50% computer-based final examination with a 45% minimum, quizzes 20% and assignments 30%. The published workload is 12.5 hours a week including three hours of lectures and weekly drop-in help sessions. It runs in Semester One and Semester Two at the City campus, is limited entry, and cannot be taken with or after any other Statistics course.
Always treat your own course outline and the exam timetable as authoritative.
Difficulty & time commitment
Is STATS100 hard, and how much time does it take?
STATS100 is manageable if you keep a weekly rhythm and treat the back half as the main event. The pattern is consistent: it starts gently and steepens, and the heaviest assessment is the part that separates grades.
The difficulty curve and the assessment weighting point the same way: the back half is harder and worth more. Front-loading effort there is the highest-return decision in the course.
Is this course for you
Who tends to do well, and who tends to struggle
You will likely do well if
- You want to understand data before you want formulas.
- You do the weekly assignments every week; they are 30% and they build the skills the exam tests.
- You can write two clear sentences about what a result shows and how sure you are.
- You are comfortable learning software by doing.
You may struggle if
- You expect a calculation-heavy course; this one is about reasoning and interpretation.
- You treat the 45% exam floor as a detail; it is the single most common way to fail a course you are passing on coursework.
- You skip drop-in help sessions when a concept does not land; they are not recorded and cannot be replayed.
- You leave the ethics and consequences material unrevised; it carries two learning outcomes.
- For every test the course teaches, write a one-line version of the null model in plain words.
- Practise reading a p-value as 'how surprising under the chance model', never as 'the probability the claim is false'.
- Keep a running list of data-ethics questions from each dataset used: consent, representation, provenance.
- Sit every quiz as if it were the exam: computer-based, timed, no notes you have not already internalised.
Syllabus
The 8 topics, topic by topic
The exam-weight marker on each topic shows where the marks concentrate. The amber topics carry the highest exam weight.
T1 · Data sources and data handling
Learning outcome 1Working with a range of data sources and getting them into shape for analysis.
T2 · Making predictions
Topic 1Using patterns in data to predict, and judging how far a prediction can be trusted.
T3 · Chance and simulation
Topic 1; course overviewComputer simulation as the tool for seeing what randomness alone would produce.
T4 · Conducting tests
Topic 2Setting up a claim, comparing data against a chance model, and reading the result.
T5 · Building models
Topic 3; learning outcome 4Mathematical representations of relationships in data, including linear models.
T6 · Communicating uncertainty
Learning outcome 5Written summaries that state what the data support and how uncertain that is.
T7 · Designing investigations
Learning outcome 6Planning, conducting and evaluating a statistical investigation.
T8 · Informing decisions, ethics and consequences
Topic 4; learning outcomes 7 and 8Responsible data practice and the social consequences of data-based decisions.
How it's assessed
Assessment structure
| Component | Weight | Format & timing |
|---|---|---|
| Final Exam | 50% | Computer-based final examination covering the whole course. Examination period. At least 45% required in this component. |
| Quizzes | 20% | Computer-based quizzes, possibly held outside lecture time including evenings. Across the semester. Individual coursework. |
| Assignments | 30% | Weekly assignments. Weekly. Individual coursework. |
- Two conditions: at least 50% overall AND at least 45% in the final examination. Strong quizzes and assignments cannot compensate for an exam under 45%.
- The exam is computer-based and worth half the grade, with its own 45% floor. The quizzes are the rehearsal: same computer-based format, same conceptual questions, spread through the semester.
This is an exam-cram course. With the exams at 50% of the grade and the final exam alone at 50%, your result is overwhelmingly decided by how well you perform under time pressure. At least 45% required in this component.
Final exam timing: During the examination period. Confirm the exact date and venue on your exam timetable.
How to actually pass it
A weekly rhythm, two checklists, and the traps to avoid
The course rewards consistency over cramming, and practice over re-reading. Here is the loop that works, then what to have nailed before each exam.
The weekly loop
Before the mid-semester checklist
- Load, clean and describe data from more than one source.
- Make a prediction from data and say how reliable it is.
- Use a simulation to show what chance alone would produce.
- Set up and read a basic test against a chance model.
Before the final heaviest topics
- Fit and interpret a linear model.
- Write a summary that communicates uncertainty correctly.
- Design and evaluate a statistical investigation.
- Identify ethical issues and social consequences in a data-based decision.
The mistakes that cost marks
Reading the p-value as the probability the claim is wrong. It describes how extreme the data are under the chance model. The exam is built around this distinction.
Confusing prediction with explanation. A model that predicts well may explain nothing. The course separates the two on purpose.
Passing on coursework, failing on the floor. A 45% exam minimum applies regardless of your total; check your exam readiness, not just your running mark.
Formula & concept sheet
The vocabulary and formulas you must own
- Population and sample
- The whole group of interest versus the part actually measured.
- Variable
- A characteristic recorded for each unit; categorical or numerical.
- Distribution
- The pattern of values a variable takes, described by shape, centre and spread.
- Simulation
- Using a computer to generate what chance alone would produce, for comparison with real data.
- Chance model
- A description of how data would arise if only randomness were operating.
- Hypothesis test
- A comparison of observed data with a chance model to judge whether an effect is plausible.
- p-value
- How extreme the observed statistic is under the chance model; small values are surprising.
- Confidence interval
- A range of plausible values for a population quantity, with a stated level of confidence.
- Linear model
- A straight-line description of how one variable changes with another.
- Data ethics
- Responsible practice around consent, representation, provenance and the consequences of decisions.
Set texts
The prescribed reading
The syllabus references map straight onto these.
STATS 100 course book
.
Where it fits
Prerequisites, related courses & why it matters
Restriction: at least 14 credits from NCEA Level 3 Mathematics or Statistics (or CIE/IB equivalent); may not be taken with, or after passing, any other Statistics course. STATS 100 is 15 points at Stage 1, limited entry, offered in Semester One and Semester Two at the City campus, and prepares students for STATS 101 and STATS 108.
Your STATS100 study toolkit
Study the course with Sia, not just read about it
Each tool already knows STATS100: your syllabus, your texts, and where the marks are. Grouped by how you study, from first contact to exam week.
FAQ
Frequently asked questions
Is STATS 100 hard?
It rates moderate. The content is deliberately conceptual and accessible, but the final exam is 50% with a 45% minimum and the published workload is 12.5 hours a week, so it is not a light option.
What is the assessment breakdown?
Final exam 50%, quizzes 20% and assignments 30%, as published on the catalogue page. The exam requires at least 45% to pass.
Who should take STATS 100 instead of STATS 101?
The catalogue recommends it for students with a limited background in statistics or mathematics; it prepares students for STATS 101 and STATS 108. It cannot be taken with or after any other Statistics course.
Is there an entry requirement?
Yes: at least 14 credits from NCEA Level 3 Mathematics or Statistics, or the CIE/IB equivalent. The course is also limited entry, first in first enrolled.
Are the quizzes in lecture time?
Not necessarily. The catalogue notes quizzes may be held at a time other than the standard lecture time, including in the evening, and that quizzes and the exam are computer-based.
Is there a textbook?
There is a course book accessed within Canvas. No external text is prescribed on the catalogue page.
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