Provides a firm grounding in the foundations of probability and statistics, the course focuses on real examples from the medical literature and popular press.Overview
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.
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.
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 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 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.
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, 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.
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.Share with friends and family!
Classes StartJun 11, 2013
Classes EndAug 18, 2013
Estimated Effort8-12 hrs/wk
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