Spring 2027

Introductory Applied Statistics for the Life Sciences

Introductory applied statistics for life sciences using R, covering probability, inference, ANOVA, linear regression, and experimental design.

offering recorded3 credits
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 6

Past student reviews · Cecile Ane, Mitchell Paukner

Cecile Ane’s group project made the class feel like 4 credits, and her homework was not enough for preparation. Mitchell Paukner’s tests accurately reflected lecture content, though students found the lectures exhausting.

What you’ll be able to do

  • Articulate the basics of probability and statistics

  • Make numeric and graphical summaries of simple data

  • Produce appropriate statistical analyses of simple data sets

  • Design simple experiments whose data will suit basic statistical analysis

  • Use RStudio, a free statistical software package, for statistical computations and graphs

  • Study and learn additional statistical methods

Source: UW Guide

Grade history

↗
average GPA
letter grades
A
AB
B
BC
C
D
F

All recorded terms · compare terms & instructors

Prerequisites

Course map

(MATH 112 and placed out ofMATH 113), (MATH 113 and placed out ofMATH 112), (MATH 112 and 113),MATH 114, 171, 211, 221, or placement inMATH 221. Not open to students with credit for STAT 302 or 324

STAT 371 used by

“Used by” includes alternatives; linked courses may have other requirements. This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree

Professors

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

AMY BEYLER is recorded teaching in Spring 2022. Recorded history may be incomplete and does not establish a future schedule.

CECILE ANE is recorded teaching in Spring 2012. Recorded history may be incomplete and does not establish a future schedule.

CHENLIANG XU is recorded teaching in Fall 2013, Spring 2014. Recorded history may be incomplete and does not establish a future schedule.

JANA RANSON is recorded teaching in Fall 2023. Recorded history may be incomplete and does not establish a future schedule.

JOHN DAVIS is recorded teaching in Fall 2014, Spring 2015, Fall 2015, Spring 2016. Recorded history may be incomplete and does not establish a future schedule.

MITCHELL PAUKNER is recorded teaching in Fall 2019. Recorded history may be incomplete and does not establish a future schedule.

NICHOLAS STEPHEN KEULER is recorded teaching in Spring 2008, Spring 2009, Fall 2016, Spring 2017. Recorded history may be incomplete and does not establish a future schedule.

SEAN KENT is recorded teaching in Fall 2018. Recorded history may be incomplete and does not establish a future schedule.

VIVAK PATEL is recorded teaching in Spring 2025, Spring 2026. Recorded history may be incomplete and does not establish a future schedule.

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction0 / 2000
LEC 002Classroom Instruction0 / 2000
LEC 003Classroom Instruction0 / 2000

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.

Meeting source records
Download part 1

Student experience

the class

Past student reviews · Sean Kent, Mitchell Paukner

Sean Kent expressed expectations clearly and was a fair grader, while Mitchell Paukner organized lectures well and provided generous grading. Both instructors were rated highly for their teaching quality.

Recent recorded grades — Spring 2025: 2.90 GPA, 37.6% A/AB (n=436 letter grades); Fall 2025: 3.06 GPA, 43.4% A/AB (n=511 letter grades); Spring 2026: 2.87 GPA, 37.2% A/AB (n=503 letter grades).

difficulty & workload

Past student reviews · Cecile Ane, Mitchell Paukner

Cecile Ane’s group project made the class feel like 4 credits, and her homework was not enough for preparation. Mitchell Paukner’s tests accurately reflected lecture content, though students found the lectures exhausting.

Past student reviews · Liam Johnston, Vivak Patel

TA Liam Johnston was highly responsive and effective in discussions, helping students understand complex material. Other students found success by reading supplemental sheets and practicing consistently rather than relying solely on lectures.

Topics

  • Probability and random variables
  • One-sample testing and confidence intervals
  • Analysis of variance and linear regression
  • Biological applications

Skills

  • Using R and RStudio for statistical computations and graphs
  • Conducting exploratory data analysis and statistical analyses
  • Designing simple experiments suitable for statistical analysis

Grades

Historical instructor

Spring 2027 · Projected

Before grades are released

– average GPA

Approximate 80% prediction interval

About this estimate

The course’s semester-average GPA, not an individual student’s grade. The center uses 5 same-season terms, weighted toward recent results.

The range uses the finite-sample 80th-percentile rank of absolute errors from earlier same-season forecasts. Each forecast uses only records from earlier terms. At least four forecasts are required; bounds are rounded outward and limited to 0–4. This is an empirical estimate: changing instructors or grading policies can reduce its coverage.

8 earlier forecasts · 0.17 GPA average error.

Grades over time

Through Spring 2027

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Spring 2026 · all course levels

GPA

Higher than % of other courses in this group.

Course GPAs · red marks this course’s range

letter grades

More recorded grades than % of other courses in this group.

Typical course in this group: letter grades.

About this comparison

1280 courses over the same term, each with at least 30 recorded letter grades. Cross-listed courses count once. GPA is not a measure of difficulty or teaching quality. The typical course is the median by recorded grade count; tied values are not counted as lower. Grade counts describe course scale, not unique students or typical section size.

Descriptions compare GPA with this group’s average: at least 0.20 higher or lower; otherwise close to average. Section size uses median recorded enrollment: small up to 30, mid-sized 31–99, large 100+. Lectures and discussion/lab sections are described separately.

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

Observations at scan time; dates do not imply when a catalog change took effect.

Download part 1
Selected offering source records
STAT 371 · Spring 2027

Introductory Applied Statistics for the Life Sciences

Recorded 2026-10-14
Raw records 
[
  {
    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:932:021160",
    "course_id": "STAT 371",
    "course_uid": "course_31fb5b7b6bf20ca63e33bd46",
    "term_id": "1274",
    "source_course_id": "021160",
    "source_subject_id": "932",
    "title": "Introductory Applied Statistics for the Life Sciences",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Not Applicable"
  }
]
Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews
Download part 1
Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1Download part 2Download part 3Download part 4
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "fd3a254370f600b6f32e813ad261c25a63a94afe0ccb20f5f9aebef707a26a36",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}