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Statistical Methods, 8th paper options

STAT115 practice questions

yes, cutline has stat115 practice questions covering the otago introduction to biostatistics, with worked solutions, and the bank is still rolling out. that spans data summaries, probability, confidence intervals, hypothesis testing, regression and study design, and you can start free without a card.

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2,730published MCQs
47worksheets and mock exams

As the catalogue lists them, checked 23 Oct 2026.

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  • L1: What is StatisticsSample 1 of 4
    Which of the following best describes statistics as a discipline?
    Answer choices
    Show answer

    Correct answer: A

    Worked solution

    Statistics is the discipline that concerns the collection, organisation, analysis, interpretation and presentation of data. Taken together, these activities amount to learning from data. Producing graphs or calculating averages are only individual parts of this larger process, statistics does not remove all uncertainty, and it is not pure mathematics divorced from data.
  • L1: Statistics and the Scientific MethodSample 2 of 4
    The scientific method is a process used to gain knowledge and understanding through the testing of hypotheses. Which statement best describes how statistics relates to the scientific method?
    Answer choices
    Show answer

    Correct answer: A

    Worked solution

    The scientific method gains knowledge and understanding by testing hypotheses, and that knowledge is gained through the collection and analysis of data. Statistics supplies much of the framework and the mechanisms for implementing this process. It does not prove hypotheses or remove all uncertainty, and it is not limited only to describing data.
  • L2: Introduction to RSample 3 of 4
    Which of the following statements about the statistical software R is correct?
    Answer choices
    Show answer

    Correct answer: A

    Worked solution

    R was developed at the University of Auckland in the early 1990s by Ross Ihaka and Robert Gentleman. It is freely available and is used around the world. A key advantage is that external packages, contributed by researchers around the world, extend its base functionality, so it is not limited to its original developers. It is a statistical programming language, not a paid product, an American commercial package, or a spreadsheet.
  • L2: Summarising DataSample 4 of 4
    A researcher records the end-diastolic heart volume (in ml) of 32 male heart attack patients. Rather than printing the full list of 32 individual values in a report, she chooses to present a small set of numerical summaries instead. What is the best reason for doing this?
    Answer choices
    Show answer

    Correct answer: A

    Worked solution

    Providing the full list of observations is one option, but with a large number of patients it is not very enlightening, and it may not even be possible to share the raw values because of privacy concerns or other ethical considerations. Numerical summaries condense the data into a more useful form. Listing is not always best, there is no legal requirement to summarise, summaries do not remove measurement error, and 32 values can certainly be entered into software.

Published MCQ practice

Teaching practice and mock exams currently listed for this paper.

Teaching practice

  • Descriptive Statistics and Data

    Worksheets: 6

    MCQs: 304

    • L1: Foundations of Statistics and BiostatisticsMCQs: 48
    • L2: Data and DiscoveryMCQs: 55
    • L3: Statistical Software in RMCQs: 52
    • L4: Data SummariesMCQs: 52
    • L5: ProbabilityMCQs: 48
    • L6: Conditional Probability and IndependenceMCQs: 49

  • Probability and Random Variables

    Worksheets: 4

    MCQs: 211

    • L7: Working with Conditional ProbabilityMCQs: 45
    • L8: Random VariablesMCQs: 54
    • L9: More on Random VariablesMCQs: 58
    • L10: Introduction to Statistical ModellingMCQs: 54

  • Distributions, Sampling and Estimation

    Worksheets: 4

    MCQs: 192

    • L11: The Normal DistributionMCQs: 51
    • L12: Sampling DistributionsMCQs: 48
    • L13: Introduction to Confidence IntervalsMCQs: 49
    • L14: Understanding Confidence IntervalsMCQs: 44

  • Hypothesis Testing and Group Comparisons

    Worksheets: 4

    MCQs: 195

    • L15: Introduction to Hypothesis TestingMCQs: 44
    • L16: Errors and Power in TestsMCQs: 53
    • L17: Difference Between Two MeansMCQs: 48
    • L18: Paired Data and CorrelationMCQs: 50

  • Regression and ANOVA

    Worksheets: 8

    MCQs: 389

    • L19: Introduction to Linear RegressionMCQs: 52
    • L20: Fitting Linear Regression ModelsMCQs: 55
    • L21: Checking the Assumptions of the Linear Regression ModelMCQs: 51
    • L22: Inference with the Linear Regression ModelMCQs: 51
    • L23: Prediction with Linear RegressionMCQs: 44
    • L24: Multiple Linear RegressionMCQs: 53
    • L25: Categorical Predictors in Regression ModelsMCQs: 48
    • L26: ANOVA in ActionMCQs: 35

  • Categorical Data and Proportions

    Worksheets: 3

    MCQs: 157

    • L27: Models for Binary DataMCQs: 54
    • L28: Inference for ProportionsMCQs: 57
    • L29: Tests of Association for Contingency TablesMCQs: 46

  • Study Design, Causation and Critical Appraisal

    Worksheets: 8

    MCQs: 392

    • L30: Statistics in Context: Research Design and Critical AppraisalMCQs: 45
    • L31: Data and Knowledge: An Indigenous People's PerspectiveMCQs: 28
    • L32: Indigenous Data Sovereignty and Co-design of StudiesMCQs: 49
    • L33: SurveysMCQs: 52
    • L34: CausationMCQs: 62
    • L35: Statistical Inference in ContextMCQs: 48
    • L36: Experiments: Critical Appraisal of an RCTMCQs: 58
    • L37: Critical Appraisal Case Study: Cohort StudyMCQs: 50

Mock exams

  • Final Exam Mocks

    Mock exams: 10

    MCQs: 890

    • Final Exam Mock 01MCQs: 89
    • Final Exam Mock 02MCQs: 89
    • Final Exam Mock 03MCQs: 89
    • Final Exam Mock 04MCQs: 89
    • Final Exam Mock 05MCQs: 89
    • Final Exam Mock 06MCQs: 89
    • Final Exam Mock 07MCQs: 89
    • Final Exam Mock 08MCQs: 89
    • Final Exam Mock 09MCQs: 89
    • Final Exam Mock 10MCQs: 89
Open STAT115 catalogue

What this list includes. Active products only. Published worksheets in visible groups, with unrestricted multiple-choice questions. Retired questions, module tests and written questions are excluded. This does not measure whole-course coverage or concept articles. This catalogue is cached. The app rechecks access before you start. .

What this paper covers

These are paper topics, not a list of published worksheets.

  • descriptive statistics and data
  • probability and random variables
  • distributions, sampling and estimation
  • hypothesis testing and group comparisons
  • regression and anova
  • categorical data and proportions
  • study design, causation and critical appraisal

How to approach STAT115

stat115 is a nice introductory paper. there are no maths prerequisites and it starts from the basics of data and probability. the only trick is staying on top of the concepts as they build on each other. practising questions keeps them fresh so the ideas stack instead of slipping.

worth
18 points (0.15 EFTS)
taught
semester 2
format
no maths prerequisite, and it uses the R software throughout

How to study STAT115 (science-based)

These are established findings, not fringe claims, and recent reviews keep confirming them (Carpenter et al., 2022; Weinstein et al., 2018). The emphasis here is on the higher-order skill of applying and transferring ideas to unseen questions, which is what separates the top HSFY grades and the study habits medicine selects for.

stat115 is an approachable introductory paper with no maths prerequisite, and it uses R throughout. its hardest skill is not calculation but choosing the right approach, so the methods below train selection and keep the building-block concepts fresh as they stack.

  1. interleave the test types

    the single hardest thing in an introductory statistics course is not running a test but knowing which test to run. interleaving, practising mixed question types rather than one at a time, trains exactly that selection skill and beats blocked practice on later tests (Rohrer & Taylor, 2007).

    if you only ever practise t-tests in the t-test chapter and chi-square in the chi-square chapter, you are always told which tool to use, which the exam will not do. a mixed set forces you to read a scenario cold and decide whether it needs a t-test, a chi-square, or a regression before you touch any numbers, and that decision is where most stat115 marks are won. (Rohrer & Taylor, 2007; Brunmair & Richter, 2019)

    Try it: mix question types in one set so you have to decide which analysis fits before you compute anything.

  2. space the concepts as they build

    statistics is cumulative: probability underpins distributions, which underpin confidence intervals and hypothesis tests, so an early gap compounds into later confusion. spacing your review, briefly re-testing earlier ideas while learning new ones, keeps the foundations available and holds more for the same effort than cramming (Cepeda et al., 2006).

    in practice this means not leaving probability and distributions behind once you move on. a few minutes re-testing them while you learn regression keeps them live, so that when a later topic leans on them you are not relearning from scratch under exam pressure. (Cepeda et al., 2006)

    Try it: briefly re-test earlier concepts like probability and distributions while learning later ones, rather than leaving them behind.

  3. attempt before you check R

    reading a worked solution or R output feels efficient, but it is a weak signal of understanding, because recognising a correct answer is far easier than generating one. attempting first, closed-book, is a desirable difficulty: it feels harder but produces stronger learning (Bjork & Bjork, 2011).

    for stat115, work out your interpretation of a result yourself before you run or read the R output, then use the output to confirm or correct. this makes you practise the reasoning the exam tests, rather than practising the much easier task of nodding along to an answer that is already on the screen. (Bjork & Bjork, 2011)

    Try it: work out the interpretation yourself before running or reading the R output, then use the output to confirm.

  4. apply the reasoning to new data

    top stat115 grades come from applying statistical reasoning to an unfamiliar dataset or scenario, choosing and justifying an approach you were not told to use, not from reciting formulae. varied retrieval practice builds this transfer to new situations (Pan & Rickard, 2018; Carpenter et al., 2022), and for HSFY students bound for medicine it is the same self-testing that predicts later exam performance (Deng et al., 2015).

    practise on scenarios you have not seen: read a fresh described study, decide which analysis fits, and interpret what a result would mean in context. because the exam gives novel scenarios rather than labelled exercises, rehearsing that judgement is what separates a solid grade from a top one. (Pan & Rickard, 2018; Carpenter et al., 2022; Deng et al., 2015)

    Try it: take an unfamiliar scenario, decide which analysis fits and why, and state what a given result would mean in context.

Flashcards, spaced repetition and Anki

The spacing effect is why flashcards work: revisiting a fact just as you are about to forget it locks it in far better than cramming (Cepeda et al., 2006). Anki is the tool most health-science students reach for, and it is good. card the "which test, and when" decisions rather than the formulas, and space them so the choice becomes automatic. Cutline pairs Anki-style flashcards for the raw facts with a qbank of exam-style STAT115 questions, so you can drill what needs memorising and then practise applying it.

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Questions about STAT115

is the stat115 bank fully live yet?

the stat115 bank is being reviewed before it goes live, so this page rolls out lecture by lecture as content is published.

will this page update automatically once more content is published?

yes, this page is driven by real published counts, so it updates automatically with no manual edits.

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The evidence

The study methods above come from established research in cognitive and educational psychology.

  1. Rohrer, D., & Taylor, K. (2007). The shuffling of mathematics problems improves learning. Instructional Science, 35(6), 481 to 498.
  2. Brunmair, M., & Richter, T. (2019). Similarity matters: A meta-analysis of interleaved learning and its moderators. Psychological Bulletin, 145(11), 1029 to 1052.
  3. Cepeda, N. J., Pashler, H., Vul, E., Wixted, J. T., & Rohrer, D. (2006). Distributed practice in verbal recall tasks: A review and quantitative synthesis. Psychological Bulletin, 132(3), 354 to 380.
  4. Bjork, E. L., & Bjork, R. A. (2011). Making things hard on yourself, but in a good way: Creating desirable difficulties to enhance learning. In M. A. Gernsbacher et al. (Eds.), Psychology and the real world (pp. 56 to 64). Worth Publishers.
  5. Pan, S. C., & Rickard, T. C. (2018). Transfer of test-enhanced learning: Meta-analytic review and synthesis. Psychological Bulletin, 144(7), 710 to 756.
  6. Carpenter, S. K., Pan, S. C., & Butler, A. C. (2022). The science of effective learning with spacing and retrieval practice. Nature Reviews Psychology, 1(9), 496 to 511.
  7. Deng, F., Gluckstein, J. A., & Larsen, D. P. (2015). Student-directed retrieval practice is a predictor of medical licensing examination performance. Perspectives on Medical Education, 4(6), 308 to 313.
  8. Weinstein, Y., Madan, C. R., & Sumeracki, M. A. (2018). Teaching the science of learning. Cognitive Research: Principles and Implications, 3, 2.

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