This note is about attempting to remove the bias brought in by using sample standard deviation estimates to estimate an unknown true standard deviation of a population. We establish there is a bias, concentrate on why it is not important to remove it for reasonable sized samples, and (despite that) give a very complete bias management solution.
I know I write a lot about coding in
R. But it is in the service of supporting statistics, analysis, predictive analytics, and data science.
Rwithout data is like going to the theater to watch the curtain go up and down.
(Adapted from Ben Katchor’s Julius Knipl, Real Estate Photographer: Stories, Little, Brown, and Company, 1996, page 72, “Excursionist Drama 2”.)
Usually you come to
R to work with data. If you think and plan in terms of data and values (including introducing more data to control processing) you will usually work in much faster, explainable, and maintainable fashion.
To illustrate this we will work an example.
He even picked the right image:
I want to share an edited screencast of my rehearsal for my recent San Francisco Bay Area R Users Group talk: