LISA (Laboratory for Interdisciplinary Statistical Analysis) provided a series of evening short courses to help graduate students use statistics in their research. The focus of these two-hour courses was on teaching practical statistical techniques for analyzing or collecting data.

September 11, 2012
Designing Experiments and Collecting Useful Data
Instructor: Jonathan Stallings

Across all disciplines, the ability to test theories by experimentation is vital for validation and discovery. When designing an experiment, the researcher hopes to maximize the obtained information by reducing wasted resources and allocating treatments in such a way as to minimize variances. Ideally, a design will account for major sources of variation so that the researcher can be confident the effects of treatments are not confounded with extraneous factors. In this course, the basic principles of experimental design will be given and specific designs discussed. The first designs introduced will be completely randomized designs, the most straightforward design when a researcher wants to test for differences among multiple treatments. Optimal blocking strategies will then be presented as a variance-reducing technique, e.g. perhaps the researcher feels a subject's gender may significantly affect observations. For each design we will discuss implementation, appropriate analysis and provide examples in SAS. If time permits we may also introduce more complicated designs tailored specifically to the researchers attending the course.

This 2 hour course is broken up into 18 parts on Vimeo.
1 - Introduction (
2 - Obs Study vs Designed Experiment (
3 - Design Example (
4 - Design Fundamentals (
5 - Lady Tasting Tea Example (Assignment) (
6 - Intro to completely Randomized Design (
7 - Analysis - Linear Model (
8 - CRD Linear Model; ANOVA; and Estimability (
9 - CRD Analysis Example in JMP (
10 - Factorial Treatment; Linear Model; ANOVA (
11 - Factorial Analysis Example in JMP (
12 - Excercises (
13 - ANCOVA; Linear Model (
14 - ANCOVA Example in JMP (
15 - Blocking (
16 - Randomized Complete Block Design (
17 - RCBD Analysis Example in JMP (
18 - Blocking vs ANCOVA (

Course files are available on the course webpage (

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