Statistics in Medicine
Welcome to the Stanford Online Statistics in Medicine Course Homepage. From this page, you can link to the different versions available using the dropdown list below.
Which Version should I select?
If you are new to the Statistics in Medicine course or would like to take the active offering again, select the "Statistics in Medicine, Summer 2014" version from the dropdown list. This version offers a Statement of Accomplishment and you are welcome to join this course if you have taken a previous version.
If you participated in the previous offering and would like to continue accessing that version, select the archived "Statistics in Medicine, Summer 2013" course from the dropdown list. Please note, you can no longer submit assignments in archived courses or earn a Statement of Accomplishment after the course end date.
About Statistics in Medicine
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.