Hello
@YanivD,
Replications are used in order to estimate more precisely pure error and parameters estimates more accurately. Adding replicates in a design enables you to lower the variance prediction and the variance of parameter estimates in your model.
You may use it in the case of a strong noise in your response, or if you need to achieve a certain level of precision for your predictions. You can also add replicates in a second run of experiments (if you realize your prediction variance is too high for example), by using the platform "Augment Design", specifying your factors and response, and then clicking on "Replicate".
There are no "hard rules" for the number of replications, as this may dependent on the topic, equipment used, precision needed, etc... Please note that you may have two options depending on the DoE platform you use (Classical or Custom Design) :
- You can specify replicates, which is the number of times all experiments will be realized independently in the DoE,
- You can specify replicate runs, which is the independent repetition of one experiment in the design.
Concerning the number of replicates (or replicate runs), I would recommend to try the different designs you have in mind (with/without replicates and different number of replicates) and compare them through the platform "Compare Designs" (in DoE / Design Diagnostics). In the comparison window, you'll have more infos on the impact of replicates on the model precision, and you'll be able to make a choice about the best compromise between experimental budget and precision of your model.
I hope this first answer will help you,
Victor GUILLER
L'Oréal Data & Analytics
"It is not unusual for a well-designed experiment to analyze itself" (Box, Hunter and Hunter)