STOR 89.2 Course Information

Class meetings: Not offered this semester.

Prerequisites:  There are no formal prerequisites for the course apart from high school mathematics, though it will be helpful if students have a general interest in science and mathematics, and are curious.

Registration: Enrollment and registration for the course is handled online.

Instructor:  Andrew B. Nobel

Office: Hanes 308   Email:   Phone: 919-962-1352.

Office Hours: TBA

Course Goals: Interpreting and acting on quantitative information that is subject to randomness or uncertainty is an increasingly important skill for citizens of technology-rich societies. This course introduces and explores some of the fundamental ideas underlying the interpretation and understanding of uncertain information, with an emphasis on the exploratory analysis of data. As the title of the course suggests, many of these ideas are rooted in common sense, and accordingly, a common sense approach to uncertainty and data analysis will be a central theme of the course. The central goal of the course is to enhance students’ statistical numeracy and, in particular, to provide students with the tools needed to critically assess the analysis of complex data sets, and to carry out more elementary analyses themselves.

Course Overview: This is a First Year Seminar intended for Freshman.  The course will begin with the traditional scientific method, the basics of statistical experiments, and a discussion of data sets.    After an introduction to the elements of probability and Bayes theorem, we will examine how humans reason about uncertainty and risk,with an emphasis on the biases that can cloud rational decision making. We will then introduce data analysis through a mix of real examples, a variety of scientific and non-scientific readings, and the presentation of some basic ideas from statistics.  A key theme will be to identify and explain common (and avoidable) mistakes that befall the analysis of data sets large and small. Topics will include

The scientific method

Controlled experiments vs. observational studies

Models for random experiments: basic probability

Reasoning under uncertainty: sources of bias

Risk assessment: medical and legal examples

Data small and big: medical testing, high-throughput technologies

Hypothesis driven vs. data driven research

An introduction to exploratory data analysis: finding and assessing patterns in data

Some basic statistics

Noise and imperfect observations

Common statistical fallacies

Ethical issues: Tuskegee and informed consent


Attendance: Students are expected to attend all lectures.  If you are unable to attend a lecture, please let the instructor know and make plans to get the notes from another student in the class.

Laptops and cell phones: Lectures will be self-contained; the use of laptops and cell phones will not be allowed.  Students will be notified in advance of rare cases in which laptops are needed for class.

Evaluation and Grading:  There will be regular reading assignments, as well as periodic written assignments and problem sets. In addition, there will be several in-class presentations, and a class project at the end of the semester.  Details about the presentations and final project will be provided two weeks or more before they are due.

Written assignments and problem sets will be posted on the course web page and will be due at regular intervals. Unless otherwise instructed, students are free to discuss assignments and problems with each other, but each student must prepare his/her final work by themselves. In particular, copying of material from other students or external sources without appropriate citation is not allowed. Each homework will be graded: late/missed assignments will receive a grade of zero. Homework will be collected at the beginning of class on the day it is due, so please be prepared to turn in your assignment on time.  In class presentations and final projects will be judged on presentation, organization, clarity, and substance.  Late/missed presentations or projects will receive a grade of zero unless students have received prior approval from the instructor.

Class participation will be judged by attendance, and the active participation of students in class discussions.

The course grade will be computed as follows:

Class participation 10%
Written assignments and problem sets 50%
In-class presentations  20%
Class project  20%

Note: There will be no midterm or final exams.

Reading Materials: Class readings will come from a variety of sources.  The required books are:

“Thinking, Fast and Slow” by Daniel Kahneman

“Calculated Risks’’ by Gerd Gigerenzer

“How Risky is it, Really?’’ by David Ropeik

In addition, there is a coursepack, available from UNC Student Stores, with selected readings from

“Statistics”, third edition, by Freedman, Pisani, and Purves

“The Periodic Table” by Primo Levi

Additional reading will come from publicly available journal articles and online resources such as Wikipedia.

Changes to the Syllabus: The instructor reserves the right to make changes to the information contained on this page.  Changes will be announced in class as early as possible.

Honor Code: Students are expected to adhere to the UNC honor code at all times.  Violations of the honor code will be prosecuted.