I would add just a bit to Victor's response. For the response variable in question (repeated measures), there are a couple of things you can do, but it does depend on how you got the 3 data points. Were they multiple measures of the same sample (e.g., measurement system)? Were they 3 samples within treatment (e.g., within batch)? Here are some options:

1. Plot the 3 data points for each treatment (Variability plot of Graph Builder). Are there any unusual data points? Are there any patterns?

2. Assess the consistency of those data points with a Range control chart. Are there any outliers?

3. After looking at the data, perhaps the appropriate summary statistics are mean and standard deviation. Both responses (or some transform of those) can be modeled. The mean would reduce the variation of the 3 repeated measures (perhaps reducing that measurement error) and the standard deviation would quantify the variation of the 3 data points.

In any case, there would be no reason to add a random term to account for the 3 measures.

"All models are wrong, some are useful" G.E.P. Box