I am neither a mathematician nor do I possess a large knowledge in maths so this could also be completely false. But I read that if you use an orthogonal function like the Chebychev-polynomial to construct the background of a set of datapoints, in my case a diffraction experiment, you will not end up in having your polynomial function starting to oscillate once you approach a higher order. I think you can see this effect whenever you try to fit something using simple polynomial functions of higher order like Excel or Origin does it. Once you approach a higher order of your function it doesn't become better but starts to oscillate between the datapoints. So your solution becomes worse at some point. I was doing a Rietveld-refinement when this happened to me. So, after reading through a few books I switched to a Chebychev-function which gave a much better solution. I tested the function by refining even more parameters and it just stopped at some point. It didn't become better or worse. So there was no oscillation happening here. For my specific case this seems to have a physical reason, being that the background parameters themselves and the background parameters plus the reflexes are highly correlated.
But appart from any physical interpretation is there a simple mathematical explanation to why the Chebychev-function is so much better at describing a function even though you over-parameterize it?