Bias Correction In Standard Deviation Estimates

John Mount explains how to perform bias correction and explains why it happens so rarely in practice:

The bias in question is falling off at a rate of 1/n (where n is our sample size). So the bias issue loses what little gravity it ever may have ever had when working with big data. Most sources of noise will be falling off at a slower rate of 1/sqrt(n), so it is unlikely this bias is going to be the worst feature of your sample.

But let’s pretend the sample size correction indeed is an important point for a while.

Under the “no bias allowed” rubric: if it is so vitally important to bias-correct the variance estimate, would it not be equally critical to correct the standard deviation estimate?

The practical answer seems to be: no. The straightforward standard deviation estimate itself is biased (it has to be, as a consequence of Jensen’s inequality). And pretty much nobody cares, corrects it, or teaches how to correct it, as it just isn’t worth the trouble.

This is a good explanation of the topic as well as the reason people make these corrections so rarely.

Related Posts

Python and R Data Reshaping

John Mount takes us through a couple of data shaping packages: The advantages of data_algebra and cdata are: – The user specifies their desired transform declaratively by example and in data. What one does is: work an example, and then write down what you want (we have a tutorial on this here).– The transform systems can print what a transform is going to […]

Read More

When to Use Different ML Algorithms

Stefan Franczuk explains the different categories of machine learning algorithms available in Talend: Clustering is the task of grouping together a set of objects in such a way, that objects in the same group are more similar to each other than to those in other groups. Clustering is really useful for identify separate groups and […]

Read More


November 2018
« Oct Dec »