How much stats training do you have by the way?

I don't know all the inventions of the number crunchers who are - in my modest opinion - making things often way too complicated, but I did get the courses we needed in physics, about probability, distributions, error propagation, least squares regression, numerical analysis, etc. And I do have experience with measuring methodology!

What surprises me on all these statistics forums is that so many people seem to have no clue what they are doing; they have learned hundreds of tricks and techniques, but they don't know what to do with them. I see questions all the time like "Should I use this or that test? Is this significant, yes or no? There is something wrong with my data, should I do this or that transformation? Should I use linear or nonlinear regression?" etc. etc., as if they are running like a chicken without a head. They just see the numbers, believe in them like they are the holy gospel, and they forget to ask the essential questions: "What is the mechanism behind the numbers? How do we expect - using plain common sense logical thinking - x to influence y?". Nobody seems to care about measurement precisions, about errors caused by transformations, about the logical validity of their model (e.g. does it make sense to extrapolate it?); they just believe what comes out of their software without having an idea about the precision of the parameters it produces. They don't question the techniques used, etc. I've seen the weirdest things, like this statistics professor who does

*linear* regression of "miles per gallon" vs "car weight", seriously (

), or all these people who still use the BMI (m/h²) while it is obvious from a physical (

*and* experimental) perspective one should use CI (m/h³), just because their professor told them to do so, etc.

That's why I wrote my FittingKVdm software: it is interactive, you see the iteration working and you get a good intuitive feeling about how the parameters change your model; you see how measurement errors influence the confidence limits, and most important: for invertible model functions, it offers a better algorithm (multidirectional regression) which is not implemented in any other program I know. You can read about my latest findings here:

https://www.researchgate.net/profile/Koen-Van-De-Moortel/research