Hi there. I’m Greg. I’m starting a trip. This is an educational journey through the concept of design of experiments (DOE). I’m doing this to better understand the company I work for (Stat-Ease), the product we create (Design-Expert® software), and the people we sell it to (industrial experimenters). I will be learning as much as I can on this topic, then I’ll write about it. So, hopefully, you can learn along with me. If you have any comments or questions, please feel free to comment at the bottom.
So, off we go. First things first.
What exactly is design of experiments (DOE)?
When I first decided to do this, I went to Wikipedia to see what they said about DOE. No help there.
“The design of experiments (DOE, DOX, or experimental design) is the design of any task that aims to describe or explain the variation of information under conditions that are hypothesized to reflect the variation.” –Wikipedia
The what now?
That’s not what I would call a clearly conveyed message. After some more research, I have compiled this ‘definition’ of DOE:
Design of experiments (DOE), at its core, is a systematic method used to find cause-and-effect relationships. So, as you are running a process, DOE determines how changes in the inputs to that process change the output.
Obviously, that works for me since I wrote it. But does it work for you?
So, conceptually I’m off and running. But why do we need ‘designed experiments’? After all, isn’t all experimentation about combining some inputs, measuring the outputs, and looking at what happened?
The key words above are ‘systematic method’. Turns out, if we stick to statistical concepts we can get a lot more out of our experiments. That is what I’m here for. Understanding these ‘concepts’ within this ‘systematic method’ and how this is advantageous.
Well, off I go on my journey!