STAT115 practice questions

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L1: What is Statistics

Which of the following best describes statistics as a discipline?

A.The discipline concerned with the collection, organisation, analysis, interpretation and presentation of data, that is, learning from data.B.The production of graphs and charts to make reports look professional.C.A set of procedures for calculating averages such as the mean and median.D.A method that removes all uncertainty from data so that conclusions are certain.E.The study of pure mathematics with no connection to data.

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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.

Published MCQ practice

Teaching practice and mock exams currently listed for this paper.

Teaching practice

Descriptive Statistics and DataWorksheets: 6MCQs: 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 VariablesWorksheets: 4MCQs: 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 EstimationWorksheets: 4MCQs: 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 ComparisonsWorksheets: 4MCQs: 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 ANOVAWorksheets: 8MCQs: 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

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how to actually 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 a genuinely 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.

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 actually 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.

space the concepts as they build

statistics is cumulative: probability underpins distributions, which underpin confidence intervals and hypothesis tests, so an early gap quietly 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.

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.

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 genuinely 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.

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.

the evidence

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

Rohrer, D., & Taylor, K. (2007). The shuffling of mathematics problems improves learning. Instructional Science, 35(6), 481–498.

Brunmair, M., & Richter, T. (2019). Similarity matters: A meta-analysis of interleaved learning and its moderators. Psychological Bulletin, 145(11), 1029–1052.

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), 354–380.

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. 56–64). Worth Publishers.

Pan, S. C., & Rickard, T. C. (2018). Transfer of test-enhanced learning: Meta-analytic review and synthesis. Psychological Bulletin, 144(7), 710–756.

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), 496–511.

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), 308–313.

Weinstein, Y., Madan, C. R., & Sumeracki, M. A. (2018). Teaching the science of learning. Cognitive Research: Principles and Implications, 3, 2.

stat115 practice questions