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, participants will learn how to read, interpret, and critically evaluate the statistics in medical studies.
The course also prepares participants 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.
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.
Participants 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 participants who need a refresher on these topics, and can also be found here.
Kristin Sainani (née Cobb) is an associate 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. In addition to this MOOC, she has taught the MOOC "Writing in the Sciences" on Coursera and Stanford Online.
Joshua Wallach graduated with a Bachelors degree in Economics from the University of California, Davis in 2012. As a current PhD student in Epidemiology and Clinical Research, he is interested in evaluating statistical and epidemiological methods, identifying and minimizing biases, and promoting reproducibility of research. Joshua is passionate about the interdisciplinary nature of epidemiology and meta-research and enjoys working as a Teaching Assistant. When not busy pursuing an academic career, Joshua loves living in Oakland and enjoys hiking, playing guitar, and working out.
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.
Yes, participants who score at least 60 percent will pass the course and receive a Statement of Accomplishment.
Participants who score at least 90 percent will receive a Statement of Accomplishment with distinction.
You should expect this course to require 8 to 12 hours of work per week.
No, readings are optional; and the use of the R statistical package is optional.
This free version of the course does not offer CME credits, but there is a fee-based CME version available as well. Go to the Stanford online CME course page for more information. You are welcome to take this free version of the course before the CME course, but note that you will still need to create an account on the CME site, pay the registration fee, and complete the CME Pre-test, Post-test, Evaluation Survey, and Activity Completion Attestation statement in order to receive your credits.