Replicates in statistics refer to the repeated, non-consecutive running of an experimental design several times. This is done to provide additional information and freedom to help understand and better estimate the variation in an experiment. It’s not the same thing as repetition. Let’s find out more.
For replicates in statistics, the design of Experiments focuses on three important concepts: randomization and repetition. You can identify factors that, if placed at different levels, could have an impact on the desired response variable in DOE. You can then predict the outcome of your response variables based on the best settings for your factors.
Your DOE will depend on the number of levels and factors. It will run as combinations of the levels. To eliminate any unwarranted noise, the order of these runs should be random. Multiple runs of the same combination will give you more data and provide a better estimation of the variation.
Replicates in statistics
Repeating a particular combination of factors and levels consecutively is called repetition. This does not increase your understanding of the variation. If you do multiple runs of a particular combination of factors or levels, it is called repetition. Repetition would have the repeatability in Measurement System Analysis while replication would be the reproducibility.
Repetition and replicates in statistics both refer to multiple response measurements that are taken using the same factor settings. Repeat measurements can be taken in the same experiment run or during consecutive runs. Replica measurements are taken in identical, but different experimental runs.
A repeat in a Design of Experiments methodology refers to a trial that is performed; each trial is then immediately repeated before moving on to the next trial condition. This method is helpful in learning about experiments. This replicates in statistics means that all the trials are completed; only then are they repeated. This is also useful for learning about experimental errors, but replication is more effective because it spreads the time out across all the experimental trials.
