Spring 2027

Introduction to Statistical Methods

An introductory course covering distributions, measures of central tendency, hypothesis testing, confidence intervals, regression, correlation, and experimental design.

offering recorded3 credits
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 6

Past student reviews · Alexander Covington

Alexander Covington assigned long weekly homework sets that carried heavy weight in the final grade, while expecting deeper knowledge of content barely covered in lectures.

What you’ll be able to do

  • Describe statistics as a discipline and why statistics can be useful for furthering scientific research

  • Demonstrate a general understanding of the core concepts of statistics, including probability, sampling distributions, estimation, confidence intervals, and hypothesis testing

  • Create useful graphs and summaries of data, and perform basic statistical analyses when appropriate, including one- and two-sample and paired tests for means, chi-squared tests, correlations, and simple linear regressions

  • Correctly interpret statistical graphs and results in terms of the original scientific context in which the data was gathered, and identify common pitfalls that lead to compromised or misleading results

  • Recognize when to seek statistical help and have a sufficient understanding of statistical terminology to communicate meaningfully with a professional statistician

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

Satisfied Quantitative Reasoning (QR) A. Not open to students with credit for STAT 302, 324, or 371.

    • Satisfied Quantitative Reasoning (QR) A.
    • not eligible with
    take all
STAT 301 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
  • All of
    • Satisfied Quantitative Reasoning (QR) A.
    • Not eligible with

Professors

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

ALEXANDER COVINGTON is recorded teaching in Spring 2018, Spring 2019, Fall 2019, Spring 2020, Fall 2020. Recorded history may be incomplete and does not establish a future schedule.

BI CHENG WU is recorded teaching in Spring 2025, Fall 2025, Spring 2026. Recorded history may be incomplete and does not establish a future schedule.

GINA BENNINGER is recorded teaching in Fall 2013, Spring 2014, Spring 2015, Spring 2016. Recorded history may be incomplete and does not establish a future schedule.

ISMOR FISCHER is recorded teaching in Fall 2006, Spring 2007, Fall 2007, Spring 2008, Spring 2009, Fall 2009, Spring 2010, Fall 2010, Spring 2011, Fall 2011, Spring 2012, Fall 2012, Spring 2013, Fall 2013, Spring 2014, Fall 2014, Spring 2015. Recorded history may be incomplete and does not establish a future schedule.

JOHN GILLETT is recorded teaching in Spring 2010, Spring 2025. Recorded history may be incomplete and does not establish a future schedule.

KAM-WAH TSUI is recorded teaching in Spring 2007, Spring 2013, Fall 2013, Fall 2014. Recorded history may be incomplete and does not establish a future schedule.

KEVIN PACKARD is recorded teaching in Fall 2010, Spring 2011, Fall 2011, Spring 2012, Fall 2012, Spring 2013, Fall 2013, Spring 2014, Fall 2014. Recorded history may be incomplete and does not establish a future schedule.

KYLE HEBERT is recorded teaching in 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 2018, Spring 2019, Fall 2021. Recorded history may be incomplete and does not establish a future schedule.

NICHOLAS STEPHEN KEULER is recorded teaching in Fall 2012, Fall 2017, Fall 2018, Spring 2019. Recorded history may be incomplete and does not establish a future schedule.

RICHARD A. JOHNSON is recorded teaching in Fall 2007, Spring 2008. Recorded history may be incomplete and does not establish a future schedule.

RYAN ZEA is recorded teaching in Fall 2015. 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

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 · John Gillett

John Gillett taught the course in 2025 using a new textbook, describing it as a manageable challenge for students who put in consistent effort.

Recent recorded grades — Spring 2025: 2.91 GPA, 37.7% A/AB (n=393 letter grades); Fall 2025: 2.84 GPA, 35.0% A/AB (n=374 letter grades); Spring 2026: 2.88 GPA, 36.8% A/AB (n=380 letter grades).

difficulty & workload

Past student reviews · Alexander Covington

Alexander Covington assigned long weekly homework sets that carried heavy weight in the final grade, while expecting deeper knowledge of content barely covered in lectures.

Past student reviews · Mitchell Paukner

Mitchell Paukner was noted for being approachable and genuinely caring about student success, creating a supportive learning environment.

Topics

  • Probability distributions, measures of central tendency, dispersion, and the normal distribution.
  • Comparing means, standard errors, confidence intervals, and the effects of departing from assumptions.
  • Least squares, regression, correlation, their assumptions and limitations, and basic experimental design.

Skills

  • Performing hypothesis tests, constructing confidence intervals, conducting regression analysis, and designing experiments.
  • Describing data distributions, calculating measures of central tendency and dispersion, and understanding the normal distribution.

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 · 0.16 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 301 · Spring 2027

Introduction to Statistical Methods

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": "1274:932:018439",
    "course_id": "STAT 301",
    "course_uid": "course_bf80347054c4234ada79c07e",
    "term_id": "1274",
    "source_course_id": "018439",
    "source_subject_id": "932",
    "title": "Introduction to Statistical Methods",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Fall, Spring, Summer"
  }
]
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 2
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "f1672b1bddc4d513bfbdfa8b7e3b40e5dc127a52c1fdd486a57f6da83092ffa9",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}