What are the benefits of adding more data to the models?

One of the frequent questions before developing a calibration is: How many samples are necessary to develop a calibration?. The quick answer is: ¡as much as possible!. Of course is obvious that they should content variability and represent as much as possible the new data can appear in the future.

 

The main sources of error are the "Irreducible error" (error from the noise of the instrument itself), the unexplained error (variance) and the Bias and they follow some rules, depending of the number of samples we have. Another thing to take into account is the complexity of the model (the number of coefficients, parameters, or terms we add to the regression).

 

Let´s look to this plot:

Now, if we add more samples tis lines are keep them as dash lines and the Bias, Variance and Total Error improves but the complexity (vertical black line) increase, and this is normal.