We learn only when we create a regularity and all that remains from our learning efforts is some sort of confortable simplification. Now, reality escapes or diverges from our regular expectations every time we want to use or enforce them to explain or predict the course of nature. In front of the inescapable gap between our regular eden and the natural hell of observations, we can take two extreme attitudes: negate or discredit reality and reduce all divergences to some sort of noise (measurement error) or try to incorporate discording data and measures in our model. Of course there is a continuum of intermediate positions which are possible between these two extrema and it is conceivable that we change/adapt our strategy according to the context, the topic, our age or mood. However, this post supports the idea that a large part of our approach to the understanding of reality can be simplified (again a regularity 🙂 by making explicit how we position ourselves in this range between ideological defense of our model and acceptation of the confutation power of data. This trade off is well known in (frequentist) statistics where the process of estimating models from data is described in terms of the bias/variance trade-off. An estimator is a generic name for describing whatever function/algorithm bringing from data to an estimate: we could generalize here to any data/observation process returning a sort of model, regularization or belief.
A biased estimator is typically an estimator which is insensitive to data: his strength derives from the intrinsic robustness and coherence as well as his weaknesses might originate in the (in)sane attitude of disregarding data or incoming evidence. A variant estimator adapts rapidly and swiftly to data and observations but it can be easily criticized for its excessive instability.
So, nothing really new, but I feel sometimes delighted in mapping attitudes, beliefs, ideologies to this trade-off (definitely another illusion of almighty regularity) or to characterize/explain differences in terms of this classification.
|On the biased side of the world||On the variance side of the world|
|German football team||Italian football team|
|Classical art||Modern art|
|Academia||Université du peuple|
|Official press||Social networks|
|Mainstream science||Scientific breakthrough|
|Parametric statistics||Nonparametric statistics|
|Expert driven||Data driven|
|Bill Gates||Steve Jobs|
And now up to you…
PS. OK, but after all, is there a better side to stay? Hum, if you thing there is, welcome on the biased side ;-). If you think it depends, welcome on the variant side of the world.