STATS100 Concepts in Statistics
The Complete Exam Bible · S2 2026STATS100 Overview
- Department of Statistics, University of Auckland
- Semester Two, 2026
- Stage 1 undergraduate course
- 15 points
STATS 100 Concepts in Statistics develops statistical reasoning through time series, proportions, classification, chance models, sampling, intervals, data ethics, linear models, normal models, tests and study design. It is taught within Department of Statistics, University of Auckland. It is Stage 1 undergraduate course.
- Best ten assignments Eleven assignments are offered but only the best ten contribute, at 3% each, to the 30% assignment total.
- Exam minimum The 50% final exam must reach 45%, and the course total must also reach 50%; these are separate checks.
- Null is a model A p-value describes how extreme the statistic is under the stated null process, not the probability that the null is true.
What you get in the full STATS100 Exam Bible
- 9 chapters, 25 pages — every examinable topic, week by week
- 12 original practice questions, each with a model answer in the Final Examination format
- 9 worked examples, solved the way STATS100 marks
- 22 computed figures, redrawn from the course’s own numbers
- Exam-morning plan for the Final Examination (50%) — what to revise last, where marks are lost
Free on this page: assessment table, chapter map, one worked example, key terms, FAQ. The rest is in the full exam bible.
How STATS100 is assessed
| Component | Weight | Format |
|---|---|---|
| Assignments | 30% | Best ten of eleven assignments, each contributing 3% to the retained total |
| Quizzes | 20% | Two quizzes worth 10% each |
| Final Examination · hurdle | 50% | A minimum examination mark of 45% is required in addition to a 50% course total |
The best ten of eleven assignments contribute 30%, two quizzes contribute 20%, and the final exam contributes 50%. Passing requires at least 50% overall and at least 45% on the final examination.
What other students are asking about STATS100
- Where can I find lecture notes or study guides for STATS100 at Auckland?
- The STATS100 study guide in AskSia Library covers Concepts in Statistics in 9 chapters, with the assessment table, a chapter-by-chapter topic map, key terms and a worked example, free to read on this page.
- Is there a final exam in STATS100, and how much is it worth?
- Yes. In the offering this AskSia Library guide is built from, Final Examination carries 50% of the STATS100 mark; the full split is Assignments 30%, Quizzes 20%, Final Examination 50%.
- What does STATS100 cover on proportions and classification?
- In AskSia Library's STATS100 guide, the Proportions and Classification chapter explains sample proportion · classification rule · misclassification rate · compute and compare proportions or classification rates with an explicit denominator.
- What does STATS100 cover on chance models and null variation?
- In AskSia Library's STATS100 guide, the Chance Models and Null Variation chapter explains chance model · null model · simulation distribution · simulate a null statistic and compare the observed result with its reference distribution.
- What does STATS100 cover on sampling variation and standard error?
- In AskSia Library's STATS100 guide, the Sampling Variation and Standard Error chapter explains sampling distribution · standard error · sample size · explain and calculate how a statistic varies across repeated samples.
What STATS100 covers
Build the course in three arcs: Time Series and Change establishes the frame, Confidence Intervals and Claims deepens it, and Hypothesis Tests, Study Design and Communication tests the complete method.
Time Series and Change
time series · time difference · temporal pattern · separate level, change and time structure before explaining a plotted patternDefine time series
The course material gives this chapter a concrete anchor: Week 1 opens with time-series displays and change, so order and lag are part of the data rather than decoration on a generic plot.
- time series
- time difference
- temporal pattern
- Applying time series
- Limits of time difference and temporal pattern
Proportions and Classification
sample proportion · classification rule · misclassification rate · compute and compare proportions or classification rates with an explicit denominatorDefine sample proportion
The course material gives this chapter a concrete anchor: Weeks 2 and 3 move from proportions to classification, making denominator choice and class-specific error the shared reasoning problem.
- sample proportion
- classification rule
- misclassification rate
- Applying sample proportion
- Limits of classification rule and misclassification rate
Chance Models and Null Variation
chance model · null model · simulation distribution · simulate a null statistic and compare the observed result with its reference distributionDefine chance model
The course material gives this chapter a concrete anchor: Week 4 introduces chance and null models as generators of comparison statistics rather than as labels attached after seeing the result.
- chance model
- null model
- simulation distribution
- Applying chance model
- Limits of null model and simulation distribution
Sampling Variation and Standard Error
sampling distribution · standard error · sample size · explain and calculate how a statistic varies across repeated samplesDefine sampling distribution
The course material gives this chapter a concrete anchor: The two-week inference transition links sample-to-population reasoning with claims, intervals and repeated-sample variability.
- sampling distribution
- standard error
- sample size
- Applying sampling distribution
- Limits of standard error and sample size
Confidence Intervals and Claims
point estimate · confidence interval · margin of error · construct an interval and translate it into a population claim tied to the sampling procedureDefine point estimate
The course material gives this chapter a concrete anchor: The current week overviews connect interval construction to the wording and limits of population claims.
- point estimate
- confidence interval
- margin of error
- Applying point estimate
- Limits of confidence interval and margin of error
Data Sources, Missingness and Ethics
data provenance · missingness rate · data ethics · audit a data source and quantify missingness before choosing a population claimDefine data provenance
The course material gives this chapter a concrete anchor: Week 7 names sourcing, exploring data and ethics in one unit, so provenance and missingness affect the statistical claim rather than appearing as an endnote.
- data provenance
- missingness rate
- data ethics
- Applying data provenance
- Limits of missingness rate and data ethics
Relationships and Linear Models
explanatory variable · fitted line · residual · fit and interpret a linear relationship while checking residual structure and extrapolationDefine explanatory variable
The course material gives this chapter a concrete anchor: Week 8 develops numeric relationships through linear models, requiring fitted values and residual evidence to remain distinct from causal language.
- explanatory variable
- fitted line
- residual
- Applying explanatory variable
- Limits of fitted line and residual
Normal Models and Standardisation
normal distribution · standard score · tail probability · standardise a value and interpret its tail location under a defensible normal modelDefine normal distribution
The course material gives this chapter a concrete anchor: Week 9 places the normal distribution after empirical relationships, supporting standardisation only where distributional shape makes it defensible.
- normal distribution
- standard score
- tail probability
- Applying normal distribution
- Limits of standard score and tail probability
Hypothesis Tests, Study Design and Communication
test statistic · study design · uncertainty communication · connect a null comparison to the study design and communicate a qualified decisionDefine test statistic
The course material gives this chapter a concrete anchor: The final three weeks join hypothesis testing, study design and communication of uncertainty, making the validity of the conclusion depend on more than the test statistic.
- test statistic
- study design
- uncertainty communication
- Applying test statistic
- Limits of study design and uncertainty communication
It carries 15 points.
It is positioned as An introductory statistics course serving multiple study pathways.
The course emphasises simulation, visual reasoning and communication of uncertainty while weekly assignments reward sustained practice rather than one calculation-only method.
Assessment in STATS100 is distributed as follows: The best ten of eleven assignments contribute 30%, two quizzes contribute 10% each for 20%, and the final examination contributes 50%.
The operational assessment conditions matter here.
A final examination contributes 50%.
The verified course material do not establish its Semester Two sitting conditions, so use the live course site and official timetable for date, venue and resources.
What makes STATS100 demanding is concrete: The difficult move is translating a visual or simulated pattern into a parameter claim while keeping sample, population, null model, uncertainty and practical meaning distinct.
Students need at least 50% overall and at least 45% on the final examination; the exam minimum is a separate component rule.
For enrolment planning, Confirm current prerequisites and restrictions on the effective Semester Two 2026 University of Auckland course page.
Build the course in three arcs: Time Series and Change establishes the frame, Confidence Intervals and Claims deepens it, and Hypothesis Tests, Study Design and Communication tests the complete method.
Worked example · free
Estimate a proportion and state its uncertainty
- 1Calculate the sample proportion as 84 divided by 120, giving 0.70.
- 1Calculate the standard error as the square root of 0.70 times 0.30 divided by 120, about 0.0418.
- 1Use two standard errors to obtain a margin of about 0.0837.
- 1Report the approximate interval 0.616 to 0.784.
- 1Tie the interval to the sampling process and note that selection bias is not repaired by this calculation.
Key terms
- Time series
- A sequence of measurements indexed in time order, analysed for level, change, seasonal structure, dependence and unusual events.
- Data ethics
- Principles governing consent, privacy, representation, fairness, stewardship and consequences across the collection and use of data.
- Proportion
- A part-to-whole ratio for a defined category, calculated using a numerator contained within its relevant denominator.
- Classification
- The assignment of observations to categories using stated features, rules or a fitted model and an explicit error criterion.
- Chance model
- A probability-based representation of how outcomes could vary under specified random conditions or a data-generating mechanism.
- Null model
- A benchmark chance model representing no effect, no association or another stated reference against which observations are compared.
- Sampling variation
- The natural difference among statistics calculated from repeated samples selected by the same sampling process.
- Confidence interval
- An interval produced by a repeated-sampling procedure designed to contain a target parameter at a stated long-run rate.
- Margin of error
- The distance from an interval's estimate to either endpoint, combining sampling uncertainty with a chosen confidence level.
- Linear model
- A model representing the conditional mean of an outcome as an intercept plus weighted predictor terms and unexplained variation.
- Normal distribution
- A symmetric bell-shaped probability distribution determined by its mean and standard deviation and used only when its fit is defensible.
- Hypothesis test
- A procedure comparing an observed statistic with its behaviour under a stated null model to assess compatibility with that benchmark.
STATS100 FAQ
How is STATS100 assessed?
The best ten of eleven assignments contribute 30%, two quizzes contribute 10% each for 20%, and the final examination contributes 50%.
Does STATS100 have a hurdle or component-level pass rule?
Students need at least 50% overall and at least 45% on the final examination; the exam minimum is a separate component rule.
Where do students usually lose marks in STATS100?
The difficult move is translating a visual or simulated pattern into a parameter claim while keeping sample, population, null model, uncertainty and practical meaning distinct.
What is the STATS100 exam or final-task format?
A final examination contributes 50%. The verified course material do not establish its Semester Two sitting conditions, so use the live course site and official timetable for date, venue and resources.
Which offering does this STATS100 guide cover?
It is aligned to Semester Two, 2026; confirm your enrolled class and timetable in the current institutional system.
Is this STATS100 resource an official university guide?
No. It is an independent STATS100 study resource; current institutional instructions remain authoritative for assessment operation.
Questions students ask in each STATS100 chapter
- What is the main task in Time Series and Change?
- Separate level, change and time structure before explaining a plotted pattern. Chapter 01 · Time Series and Change
- What is the main task in Proportions and Classification?
- Compute and compare proportions or classification rates with an explicit denominator. Chapter 02 · Proportions and Classification
- What is the main task in Chance Models and Null Variation?
- Simulate a null statistic and compare the observed result with its reference distribution. Chapter 03 · Chance Models and Null Variation
- What is the main task in Sampling Variation and Standard Error?
- Explain and calculate how a statistic varies across repeated samples. Chapter 04 · Sampling Variation and Standard Error
- What is the main task in Confidence Intervals and Claims?
- Construct an interval and translate it into a population claim tied to the sampling procedure. Chapter 05 · Confidence Intervals and Claims
- What is the main task in Data Sources, Missingness and Ethics?
- Audit a data source and quantify missingness before choosing a population claim. Chapter 06 · Data Sources, Missingness and Ethics
- What is the main task in Relationships and Linear Models?
- Fit and interpret a linear relationship while checking residual structure and extrapolation. Chapter 07 · Relationships and Linear Models
- What is the main task in Normal Models and Standardisation?
- Standardise a value and interpret its tail location under a defensible normal model. Chapter 08 · Normal Models and Standardisation
- What is the main task in Hypothesis Tests, Study Design and Communication?
- Connect a null comparison to the study design and communicate a qualified decision. Chapter 09 · Hypothesis Tests, Study Design and Communication
How to study for the exam
Retrieve the course map, practise the recurring method—identify the population, variables and data-generating process, choose a descriptive, simulation or inferential representation, calculate transparently and express the conclusion with its uncertainty, design and ethical boundary—on changed scenarios, and verify every operational assessment detail in the live institutional system.
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