Thanks @J_Asscher and @Phil_Kay for this session, and to all Users who participated!
Video:
Resoults from the Poll Questions:
The discussion continued though the chat:
Q: Do you always accept the default number of whole plots? Would you ever increase it?
A1: Very useful indeed, but sometimes a bit more problematic for modelling
A2: You may have to increase the number of whole plot if you want more power and more precision in the estimates of your main effects for factors that are hard to change
Q: What is the implication of running a standard design by grouping experiments? is it ever acceptable?
A1: You may have lurking variables influencing your responses, like time, temperature, operator, etc... and the error is not random anymore (it may correspond to changes due to the "block changes"), which may hide some important factors or reveal non-important ones correlated to lurking variables
A2: imagine if you make two blocks and in the first you have X1 as -1 and on the second part as +1. If you have an hidden effect ore something degrade over time that increase over time, this effect is attributed to X1 even if its not. This can lead to false models.
Some further information was shared during the session:
- White Paper on Split Plot: https://www.stat.purdue.edu/~kuczek/stat514/Split%20plot%20example.pdf
- Follow Phil on LinkedIn: #DOEbyPhilKay
- Follow Victor Guiller on LinkedIn: https://www.linkedin.com/in/victorguiller/
