Correlation is not causality. You've probably heard that before in any number of regression classes. If you want to infer causality from data, then the best way is to use randomized experiments. Maybe it is the only way to be sure.
In experimental design we look at how to choose the data that we will gather. In addition to being able to make causal conclusions, we also look at how to maximize the statistical efficiency of the generated data set.
Experimental design as a subject is about 100 years old. The methods in this course date back to agricultural field trials. Since then the ideas have seen use in medicine, manufacturing, quality control, computer aided design and electronic commerce. Each new field takes the previous methods and then starts adapting them. Possibly the first clinical trial was that of James Lind in 1747 showing that citrus is effective against scurvy. (It was not immediately adopted.)
There will be some problem sets, a midterm on Thursday October 22 and a project. The project will involve designing, carrying out and analyzing a real experiment. This can be from your every day life: cooking, hobbies, exercise routines, etc. There are ordinarily about 4 problem sets. As I announced in class, due to the pandemic it seems better to have a larger number of smaller problem sets.
This Mark Rober video (might serve an ad) describes an experiment to study which animals (snake vs turtle vs tarantula) are more likely to be run over by vehicles. The results are interesting. It is also funny. (I don't recognize the characters that appear near the end though.)
- Learn the main/classical methods of experimental design so that when it comes time to gather data you can work out the right choice.
- Exposure to the research frontier in DoE: A/B testing, computer experiments, design for high dimensional regression.
- Do a designed statistical experiment from conception to execution to analysis.
see page 2 of the course announcement. I'm expecting and hoping for two guest lectures to displace two of the post-midterm topics.
Online Tue & Thu 2:30 to 3:50 Lectures at PhD level, homework at MS level.
3 units and letter grade or CR/NC.
- Art Owen
- Sequoia Hall 130
- My userid is owen at the address stanford.edu
- Office hour: Weds 1:30 - 2:30
Some links below. More may be in canvas.
HW 50%. Midterm 25%. Final project 25%.
I expect to send a small number of important emails about problem sets and the homework there. Most other announcements will be made in class. If you email me about the class, be sure to have stat 363 or stat 263 in your subject line. Otherwise, your email won't show when I search for course related emails.Late penalties apply:
We will count days late on each problem set. Each day late is penalized by 10% of the homework value. Homework more than 3 days late will ordinarily get 0. Upload to gradescope within canvas. For sickness, interviews and other events, up to 3 late days (4 in pandemic years) total are forgiven at the end of the quarter. (Work late enough to get zero does not get redeemed though.)