As my loyal reader knows, I am working with Lily Zhao (Yale) to calibrate the *EXPRES* spectrograph. Our approach is non-parametric: We can beat any polynomial calibration with an interpolation (we are using splines, but one could also use a Gaussian Process or any other method, I think). The funniest thing happened today, which surprised me, but shouldn't have! When Zhao plotted a histogram of the differences between our predicted line locations (from our interpolation) and the observed line locations (of held-out lines, held out from the interpolation), they were always redshifted! There was a systematic bias everywhere. We did all sorts of experiments but could find no bug. What gives? And then we had a realization which is pretty much *Duh*:

If you are doing linear interpolation (and we were at this point), and if your function is monotonically varying, and if your function's first derivative is also monotonically varying, the linear interpolator will always be biased to the same side! Hahaha. We switched to a cubic spline and everything went unbiased.

In detail, of course, interpolation will *always* be biased. After all, it does not represent your beliefs about how the data are generated, and it certainly does not represent the *truth* about how your data were generated. So it is always biased. It's just that once we go to a cubic spline, that bias is way below our precision and accuracy (under cross-validation). At least for now.