STATS101: pass the exams, not just read the notes
Your complete guide to University of Auckland's introduction to statistics course. See where the marks are, work real practice questions, and study with an AI tutor that knows STATS101.
Sia generates STATS101 practice questions, walks through types of investigations and data collection step by step, and quizzes you on the material the exam weights most heavily.
Worked example
A 95% confidence interval for the mean difference in test scores between two teaching methods is (-1.2, 4.8). What may be concluded at the 5% significance level?
Recall the link between intervals and tests. A 95% confidence interval contains exactly those values of the parameter that would not be rejected by a two-sided test at the 5% level.
State the conclusion in the correct language. We do not reject the null hypothesis of no difference at the 5% level. That is not the same as proving the methods are identical — the interval is wide, and a true difference as large as 4.8 points is equally consistent with the data.
Note what the interval also tells you. Being mostly positive is not evidence of significance; what matters is whether zero is excluded. The width here says the study lacked precision, which is a more useful finding than the test result alone.
The trap: Option B treats 'mostly positive' as significance. The test is whether zero is excluded, not where the bulk of the interval sits. Option C reports the point estimate as if it were established fact and ignores the uncertainty the interval exists to express. Option D is the overcorrection — intervals and tests are two views of the same inference, and reading significance off an interval is standard practice. classic slip!
One exam decides 50% of your grade. Dual pass: at least 45% required in this component alone. This whole page is built around that.
Overview
What STATS101 is, and where it sits
STATS 101 is the University of Auckland's general introduction to statistics, and the Department of Statistics is unusually direct about who it is for: anyone who will ever have to collect or make sense of data, either in their career or private life. It is not a service course for mathematicians.
The published emphasis is on data analysis and the background concepts needed to analyse data successfully, on extrapolating from patterns in data to more generally applicable conclusions — statistical inference — and on communicating results to others. The technical topics follow that arc: types of investigations, data collection, tools for exploring and summarising data, proportions, then the inference toolkit of confidence intervals, randomisation tests, statistical significance, t-tests and P-values, and finally analysing relationships through group comparison, one-way ANOVA, simple linear regression, correlation and the chi-square test for tables of counts.
The rule that decides outcomes is the dual pass requirement. You must obtain at least 50% overall and at least 45% in the final examination alone. Coursework that is strong enough to carry a weak final will not save you, and that is the single most important thing to know before planning your semester.
Always treat your own course outline and the exam timetable as authoritative.
Difficulty & time commitment
Is STATS101 hard, and how much time does it take?
STATS101 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 treat the final examination as the gate it is: 45% there is required no matter how good your coursework.
- You can state what a study design supports and what it does not, which is the reasoning the course is built on.
- You interpret rather than compute — knowing what a P-value or interval means matters more here than arithmetic speed.
- You keep up with assignments and quizzes; at 30% they are the buffer that lets the final be about passing rather than rescuing.
You may struggle if
- You rely on coursework marks to carry you. The dual pass rule specifically prevents that.
- You read a P-value as the probability that the hypothesis is true.
- You confuse statistical significance with practical importance.
- You memorise procedures without understanding which situation each applies to, which the exam is designed to test.
- For every inference procedure, write down what it assumes and what its conclusion is licensed to say. That mapping is the course.
- Practise reading confidence intervals as tests: does the interval exclude the null value, and what does its width say about precision?
- Rehearse stating conclusions in context and in plain language — the course explicitly values communicating results to others.
- Sit past final examinations under time, because the 45% threshold makes exam technique a pass or fail matter rather than a grade matter.
Syllabus
The 12 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 · Types of investigations
Course descriptionObservational studies versus experiments, and what each design can support.
T2 · Data collection and sampling
Course descriptionHow data are gathered, and why the collection method constrains every later conclusion.
T3 · Exploring and summarising data
Course descriptionGraphical and numerical tools for seeing what is in a dataset before testing anything.
T4 · Proportions
Course descriptionEstimating and comparing proportions, the simplest full inference problem.
T5 · Confidence intervals and conveying uncertainty
Course descriptionBuilding intervals and interpreting what they do and do not claim.
T6 · Randomisation tests
Course descriptionTesting by resampling, and the logic of comparing observed data to chance.
T7 · Statistical significance and P-values
Course descriptionWhat a P-value measures, and the errors that follow from misreading it.
T8 · t-tests
Course descriptionInference for means using the t-distribution, for one sample and for paired data.
T9 · Comparing groups
Course descriptionInference for the difference between two group means.
T10 · One-way analysis of variance
Course descriptionExtending group comparison beyond two groups.
T11 · Simple linear regression and correlation
Course descriptionFitting and interpreting a straight-line relationship, and measuring its strength.
T12 · Tables of counts and the chi-square test
Course descriptionTesting association between categorical variables.
How it's assessed
Assessment structure
| Component | Weight | Format & timing |
|---|---|---|
| Final examination | 50% | Final examination covering the whole course. Examination period. Dual pass: at least 45% required in this component alone. |
| Test | 20% | Mid-semester test. Mid-semester. Summative. |
| Assignments and quizzes | 30% | Assignments and quizzes across the semester. Across the semester. Continual assessment. |
- You must obtain at least 50% overall AND at least 45% in the final examination alone. The department also publishes an alternative weighting of final examination 60%, test 10%, assignments and quizzes 30%; the option applied is the one more favourable to you, but the 45% final examination threshold applies either way.
- The dual pass rule is the defining feature. Coursework cannot compensate for a final below 45%, no matter how strong it is. Note also that the alternative weighting raises the final to 60% rather than lowering it, so the examination is never less than half the grade.
This is an exam-cram course. With the exams at 70% of the grade and the final examination alone at 50%, your result is overwhelmingly decided by how well you perform under time pressure. Dual pass: at least 45% required in this component alone.
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
- Distinguish observational studies from experiments and state what each supports.
- Explore and summarise a dataset with the appropriate graphical and numerical tools.
- Estimate and compare proportions.
- Construct and correctly interpret a confidence interval.
Before the final heaviest topics
- Carry out randomisation tests and t-tests and interpret P-values correctly.
- Compare two or more groups, including one-way analysis of variance.
- Fit and interpret a simple linear regression, and distinguish correlation from causation.
- Apply the chi-square test to a table of counts and state the conclusion in context.
The mistakes that cost marks
Misreading the P-value. A P-value is the probability of data this extreme if the null hypothesis were true, not the probability that the null hypothesis is true.
Significance read as importance. A tiny effect can be statistically significant in a large sample. The course asks for conclusions in context, which means addressing size as well as significance.
Ignoring the dual pass rule. At least 45% in the final examination alone is required. Students who plan around their overall mark discover this too late.
Correlation treated as causation. Regression describes association. Only the study design, not the strength of the fit, can support a causal claim.
Formula & concept sheet
The vocabulary and formulas you must own
- Observational study and experiment
- A design that measures what occurs, versus one that assigns treatments; only the latter supports causal conclusions.
- Sampling variability
- The variation in a statistic from sample to sample, and the reason inference is needed at all.
- Confidence interval
- A range of parameter values consistent with the data at a stated confidence level, expressing precision as well as location.
- Randomisation test
- A test that compares the observed result to the distribution produced by reshuffling the data under the null hypothesis.
- P-value
- The probability of observing data at least as extreme as the sample, assuming the null hypothesis is true.
- Statistical significance
- The judgement that observed data are unlikely under the null hypothesis; distinct from practical importance.
- t-test
- An inference procedure for means when the population standard deviation is unknown and estimated from the sample.
- One-way analysis of variance
- A procedure for comparing means across more than two groups simultaneously.
- Least-squares regression
- Fitting a straight line by minimising the sum of squared vertical deviations from the data.
- Correlation coefficient
- A standardised measure of the strength and direction of a linear relationship, bounded between minus one and one.
- Chi-square test
- A test of association between categorical variables using a table of observed counts.
- Dual pass requirement
- This course's rule that both an overall threshold and a separate final-examination threshold must be met.
Common acronyms: ANOVA · CI · UPI.
Where it fits
Prerequisites, related courses & why it matters
No prerequisite. STATS 101 is worth 15 points and is taught in Summer School, Semester One and Semester Two at the City campus. Restrictions: you may take only one of STATS 101, 102, 107, 108 or 191. STATS 101G may not be taken for General Education alongside a prior or concurrent enrolment in COMPSCI, ENGGEN, ENGSCI, INFOSYS, MATHS, PSYCH or STATS.
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FAQ
Frequently asked questions
Is STATS 101 hard?
It rates moderate. The department deliberately teaches concepts rather than formula manipulation, which lowers the barrier. What raises the difficulty is the dual pass rule: you need at least 45% in the final examination alone regardless of your coursework.
What is the assessment breakdown?
Final examination 50%, test 20%, assignments and quizzes 30%. The department also publishes an alternative weighting of 60/10/30. Under either, you must obtain at least 50% overall and at least 45% in the final examination alone.
What is the difference between STATS 101 and STATS 108?
The syllabus is the same. STATS 108 Statistics for Commerce is the standard Stage I statistics course for the Faculty of Business and Economics and for Arts students taking Economics, and it places more emphasis on examples from commerce. You may take only one of them.
Do I need strong mathematics?
No. The department states directly that if your idea of fun is copying formulae off blackboards you probably will not like their courses, and it offers help services for students worried about maths. The emphasis is on reasoning with data.
What textbook do I need?
Materials produced by the department are available from the Student Resource Centre and on Canvas. Wild and Seber's Chance Encounters is an optional reference rather than a required text.
How many points is it, and when is it taught?
15 points, taught in Summer School, Semester One and Semester Two at the City campus.
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