HRP258: Statistics in Medicine

Kristin Sainani, Michael Hurley (TA), Rajhansa Sridhara (TA), Michael McAuliffe (Instructional Technologist), Stanford

Provides a firm grounding in the foundations of probability and statistics, the course focuses on real examples from the medical literature and popular press.


About This Course

This course aims to provide a firm grounding in the foundations of probability and statistics. Specific topics include:

1. Describing data (types of data, data visualization, descriptive statistics)
2. Statistical inference (probability, probability distributions, sampling theory, hypothesis testing, confidence intervals, pitfalls of p-values)
3. Specific statistical tests (ttest, ANOVA, linear correlation, non-parametric tests, relative risks, Chi-square test, exact tests, linear regression, logistic regression, survival analysis; how to choose the right statistical test)

The course focuses on real examples from the medical literature and popular press. Each week starts with "teasers," such as: Should I be worried about lead in lipstick? Should I play the lottery when the jackpot reaches half-a-billion dollars? Does eating red meat increase my risk of being in a traffic accident? We will work our way back from the news coverage to the original study and then to the underlying data. In the process, students will learn how to read, interpret, and critically evaluate the statistics in medical studies.

The course also prepares students to be able to analyze their own data, guiding them on how to choose the correct statistical test and how to avoid common statistical pitfalls. Optional modules cover advanced math topics and basic data analysis in R.

Course Syllabus

Week 1 - Descriptive statistics and looking at data
Week 2 - Review of study designs; measures of disease risk and association
Week 3 - Probability, Bayes' Rule, Diagnostic Testing
Week 4 - Probability distributions
Week 5 - Statistical inference (confidence intervals and hypothesis testing)
Week 6 - P-value pitfalls; types I and type II error; statistical power; overview of statistical tests
Week 7 - Tests for comparing groups (unadjusted); introduction to survival analysis
Week 8 - Regression analysis; linear correlation and regression
Week 9 - Logistic regression and Cox regression


There are no prerequisites for this course.

Students will need to be familiar with a few basic math tools: summation sign, factorial, natural log, exponential, and the equation of a line; a brief tutorial is available on the course website for students who need a refresher on these topics.

Course Staff

Kristin Sainani

Kristin Sainani (née Cobb) is a clinical assistant professor at Stanford University. She has taught statistics and writing at Stanford for more than a decade and has received several Excellence in Teaching Awards from the graduate program in epidemiology. She received her MS in statistics and her PhD in epidemiology from Stanford University; she also received a certificate in science writing from the University of California, Santa Cruz.

Dr. Sainani specializes in teaching and writing about science and statistics. She is the statistical editor for the journal Physical Medicine & Rehabilitation; and she writes a statistics column, Statistically Speaking, for this journal. She also authors the health column Body News for Allure magazine; and she writes about health and science for a variety of other publications. She taught her first MOOC called "Writing in the Sciences" on Coursera in the fall of 2012.

Michael Hurley (TA)

Michael completed his Bachelors degree in Materials Science and Engineering from MIT in 2010, and a Masters degree in Clinical Epidemiology at Stanford University in 2012. He is currently a first year medical student at Stanford. His research interests lie in quantifying patient outcomes and the identification of risk factors for disease and post-operative complications using large clinical data sets. At Stanford, he serves as a Teaching Assistant for numerous biostatistics courses and is involved in teaching and tutoring in various other ways. Michael enjoys cooking and traveling in his free time.

Rajhansa Sridhara (TA)

Rajhansa completed his Bachelors and Masters in Aerospace Engineering from the Indian Institute of Technology, Bombay in 2011. He then worked for the Global Risk Management division of American Express, where he was one of the analysts responsible for setting the firm's credit risk management strategies based on extensive statistical analysis of past transaction data. He started his MS in Management Science & Engineering at Stanford in 2012. In Stanford, Rajhansa is a tutor for learning-disabled students through the Office of Accessible Education. He also serves on the university's Judicial Affairs panel, and is a consultant for Stanford Consulting. In his free time, he likes to read, learn new languages, and watch westerns.

Michael McAuliffe (Instructional Technologist)

Mike McAuliffe is an Instructional Technologist in EdTech, IRT for the Stanford University School of Medicine. He supports a wide range of educational technology operations, projects, and initiatives in support of teaching, learning, and research.

Mike joined the School of Medicine in August 2012 and dedicates the majority of his time to the Stanford Medicine Interactive Learning Initiative (SMILI). In this role, Mike collaborates with SoM faculty to design and produce video content for online/hybrid courses delivered to undergraduate medical education, online courses for continuing medical education, online materials for residents and fellows, and MOOCs. Mike also provides instructional design, graphic design, and project planning support to faculty.

Frequently Asked Questions

Will I get a Statement of Accomplishment?

Yes, students who score at least 60 percent will pass the course and receive a certificate.
Students who score at least 90 percent will receive a certificate with distinction.

How much of a time commitment will this course be?

You should expect this course to require 8 to 12 hours of work per week.

Any additional textbooks/software required?

No, readings are optional; and the use of the R statistical package is optional.

Share with friends and family!
  1. Course Number

  2. Classes Start

    Jun 11, 2013
  3. Classes End

    Aug 18, 2013
  4. Estimated Effort

    8-12 hrs/wk
  • 11 June 2013
Course properties:
  • Free:
  • Paid:
  • Certificate:
  • MOOC:
  • Video:
  • Audio:
  • Email-course:
  • Language: English Gb


No reviews yet. Want to be the first?

Register to leave a review

More on this topic:
Coursera_image_maayan Network Analysis in Systems Biology
An introduction to data integration and statistical methods used in contemporary...
Penicillin-vials_small Antimicrobial Stewardship: Optimization of Antibiotic Practices
Internet Enduring Material Sponsored by: Stanford University School of Medicine...
Pgzmw8h7sloomdvdplvdg3rmh7xcngtc6w5j47_ezplh61qggi0pzzoc-vrvuqpxmoggngjgbcdd-nwc0og=s0#w=1724&h=1060 Intro to Data Science. Learn What It Takes to Become a Data Scientist
What does a data scientist do? In this course, we will survey the main topics...
97278_1270_5 Excel 2013 Tutorial - Online Excel Course - Udemy
Get the most out of Excel by focusing on tools that maximize productivity and...
18-443s09 Statistics for Applications (Spring 2009)
This course is a broad treatment of statistics, concentrating on specific statistical...
More from 'Medicine & Health':
Regular_a6449a4b-03d8-458a-9f30-2cd8e2dced5e Antimicrobial Stewardship: Managing Antibiotic Resistance
Understand antibiotic resistance, and how antimicrobial stewardship can slow...
Regular_bbb6ec97-7f45-4c43-b7e2-010e67566246 Genomic Medicine: Transforming Patient Care in Diabetes
Learn how developments in genomics are transforming our knowledge and treatment...
Regular_73805f71-1fa4-4153-852b-5a0704e3ea0f Essentials of Good Pharmacy Practice: The Basics
Discover the essentials of the Good Pharmacy Practice (GPP) guidelines and learn...
Regular_25cd96a7-82e5-40e2-b070-d432c10bbd7e Non-Invasive Prenatal Testing (NIPT): An Introduction for Healthcare Professionals
Get broad insight into the key issues surrounding Non-Invasive Prenatal Testing...
Regular_d6b049a5-29eb-411f-8ccd-21b8524dc8d6 Health Systems Strengthening
The greatest challenges and advances in global health demand systems strengthening...
More from 'Stanford':
Nlp-logo CS224d: Deep Learning for Natural Language Processing
Natural language processing (NLP) is one of the most important technologies...
Visionlablogo CS231n: Convolutional Neural Networks for Visual Recognition
Computer Vision has become ubiquitous in our society, with applications in search...
Databases DB: Introduction to Databases
Learn about Databases, one of the most prevalent technologies underlying internet...
Envphys_water EP101: Your Body in the World: Adapting to Your Next Big Adventure
Discover the amazing adaptability of the human body to environmental stressors...

© 2013-2019