STAT371: pass the exams, not just read the notes
Your complete guide to University of Wisconsin-Madison's introductory applied statistics for the life sciences course. See where the marks are, work real practice questions, and study with an AI tutor that knows STAT371.
Sia generates STAT371 practice questions, walks through exploratory data analysis and probability step by step, and quizzes you on the material the exam weights most heavily.
Worked example
Two independent groups of 20 mice each are fed diets A and B. Mean weight gain is 12.0 g (SD 3.0) for A and 10.0 g (SD 3.0) for B. What is the approximate t statistic for the difference in means?
The standard error of the difference is the square root of (3.0 squared / 20 + 3.0 squared / 20) = square root of 0.9, about 0.95 g.
With about 38 degrees of freedom this gives a two-sided p-value near 0.04, so the difference is significant at the 5% level.
Option B divides by the SD instead of the SE; option C divides by the SE of one mean only.
The trap: Dividing by a single sample's SD rather than the standard error of the difference between two means. classic slip!
One exam decides 38.5% of your grade. Same rule. This whole page is built around that.
Overview
What STAT371 is, and where it sits
STAT 371 Introductory Applied Statistics for the Life Sciences is UW-Madison's statistics gateway for biology, nutrition and other life-science majors. The offering documented here is the Summer 2026 fully online section taught by John Gillett with teaching assistants Soumen Ghosh and Zhifeng Chen; the fall and spring sections are in person and may weight differently, so check the current course page.
Each week's Canvas module pairs recorded lectures and fill-in-the-blank notes with one or two repeatable practice quizzes and one or two homework sets in R. The topic sequence is exploratory data analysis, probability and random variables, one-sample tests and confidence intervals, the role of assumptions and sample size, two-sample inference, basic experimental design, analysis of variance, linear regression and goodness of fit, all with biological applications.
The 400 points are 48 for quizzes and 48 for homework (best 12 of 14 each), 150 for a 75-minute online midterm on 13 July 2026 and 154 for a two-hour online final on 5 August 2026. Grades are the higher of a fixed percentage scale and a percentile scale.
Always treat your own course outline and the exam timetable as authoritative.
Difficulty & time commitment
Is STAT371 hard, and how much time does it take?
STAT371 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 are a life-science major who wants statistics tied to biological examples.
- You keep twice-weekly deadlines in a compressed summer term.
- You are comfortable adapting example R code rather than writing from scratch.
- You use the live Zoom Q&A rather than getting stuck alone.
You may struggle if
- You treat the repeatable quizzes as proof of mastery; the course page says they are not.
- You fall a week behind; two deadlines a week leave no slack in six weeks.
- You never open RStudio until the homework is due.
- You want a calculus-based course; that is STAT 324.
- After each homework, write the R command and the conclusion sentence side by side.
- Keep a one-page map of which test fits which data type and design.
- Practise reading ANOVA and regression output until you can locate the p-value and effect size instantly.
- Use the Learning Center's 371 practice sessions when offered in fall and spring.
Syllabus
The 9 topics, block by block
The exam-weight marker on each topic shows where the marks concentrate. The amber topics carry the highest exam weight.
T1 · Exploratory data analysis
Course notes, block 1Graphs, summaries and the shape of biological data.
T2 · Probability and random variables
Course notes, block 2Binomial and normal distributions, expected value.
T3 · Sampling distributions
Course notes, block 3The distribution of the sample mean and the central limit theorem.
T4 · One-sample inference
Course notes, block 4Confidence intervals and t tests; the role of assumptions and sample size.
T5 · Two-sample inference
Course notes, block 5Independent and paired comparisons of means and proportions.
T6 · Experimental design
Course notes, block 6Randomisation, replication, blocking and confounding.
T7 · Analysis of variance
Course notes, block 7One-way ANOVA and multiple comparisons.
T8 · Linear regression
Course notes, block 8Fitting, interpreting and checking a simple regression in R.
T9 · Goodness of fit and contingency tables
Course notes, block 9Chi-squared tests for categorical data.
How it's assessed
Assessment structure
| Component | Weight | Format & timing |
|---|---|---|
| Canvas quizzes (best 12 of 14, repeatable) | 12% | Twice-weekly Canvas quizzes, repeatable for practice; 14 quizzes at 4 points, best 12 count. Twice weekly. Lowest two dropped. |
| Homework (best 12 of 14) | 12% | Twice-weekly homework in Canvas using R via RStudio; 14 sets at 4 points, best 12 count. Twice weekly. Your own code and writing. |
| Midterm Exam (online, 75 minutes, 13 July 2026) | 37.5% | Online Canvas exam, 75 minutes, taken in a window of your choice on the day. Monday 13 July 2026. No make-up except a documented serious problem. |
| Final Exam (online, 2 hours, 5 August 2026) | 38.5% | Online Canvas exam, two hours, taken in a window of your choice on the day. Wednesday 5 August 2026. Same rule. |
- The four components sum to 100 and there is no separate hurdle. Your grade is the higher of the percentage scale (A 92+, AB 88, B 82, BC 78, C 70, D 60) and the percentile scale (A for the top 25%). No make-ups except a documented serious problem.
- A 75-minute online midterm (13 July 2026) on the first half and a two-hour online final (5 August 2026) covering everything; each is a Canvas quiz taken in a time window you choose that day. Expect R output to interpret.
This is an exam-cram course. With the exams at 76.0% of the grade and the final exam (online, 2 hours, 5 august 2026) alone at 38.5%, your result is overwhelmingly decided by how well you perform under time pressure. Same rule.
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
- Summarise data graphically and numerically and describe shape.
- Compute binomial and normal probabilities.
- Explain the sampling distribution of the mean.
- Build a confidence interval and run a one-sample t test, stating assumptions.
Before the final heaviest topics
- Choose between independent and paired two-sample tests.
- Run and interpret a one-way ANOVA.
- Fit a simple regression, interpret the slope and check residuals.
- Run a chi-squared goodness-of-fit or independence test.
The mistakes that cost marks
Wrong test for the design. Paired data analysed as independent samples is the classic exam error.
Ignoring assumptions. A stated learning outcome is knowing when assumptions fail and when to call a statistician.
Copying code without reading output. Exams ask you to interpret R output, not to run it.
Teaching team
Who teaches STAT371
The bios below are factual. We do not rate lecturers; any star ratings are submitted by students who have taken STAT371.
Teaching team as listed in the course materials reviewed. AskSia does not rate lecturers; star ratings are submitted by students who have taken STAT371.
Formula & concept sheet
The vocabulary and formulas you must own
- Standard error of the mean
- SD divided by the square root of n.
- t statistic
- Estimate minus null value, divided by its standard error.
- Confidence interval
- Estimate plus or minus a t multiple of the SE.
- Paired t test
- A one-sample t test on within-pair differences.
- ANOVA F statistic
- Between-group variance over within-group variance.
- Least-squares slope
- Change in the fitted response per unit of the predictor.
- Residual
- Observed minus fitted value.
- Chi-squared statistic
- Sum of (observed minus expected) squared over expected.
- Type I error
- Rejecting a true null hypothesis.
Set texts
The prescribed reading
The syllabus references map straight onto these.
An Introduction to Statistical Methods and Data Analysis
.
Where it fits
Prerequisites, related courses & why it matters
Prerequisite: MATH 112 and 113, or MATH 114, 171, 211, 221 or placement into MATH 221. Not open to students with credit for STAT 302 or 324. 3 credits; Summer 2026 section fully online.
Your STAT371 study toolkit
Study the course with Sia, not just read about it
Each tool already knows STAT371: 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 STAT 371 hard?
Moderate on the six-factor rubric. The techniques are introductory, but the summer session is fast and the two exams are 76% of the grade.
What is the assessment breakdown?
Canvas quizzes 12%, homework 12%, midterm 37.5%, final 38.5% (48/48/150/154 of 400 points), per the instructor's Summer 2026 course page.
Who teaches it?
John Gillett (Teaching Faculty), with teaching assistants Soumen Ghosh and Zhifeng Chen in Summer 2026.
Do I need to learn R?
You use R in RStudio by copying and modifying example code; the course does not teach programming as such.
Is there a textbook?
No required text. Course notes are provided; Ott and Longnecker's Introduction to Statistical Methods and Data Analysis is optional.
Can I take it after STAT 301?
No. STAT 371 is not open to students with credit for STAT 302 or 324, and 301 is not open to students with 371 credit; pick one gateway.
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