# Category Archives: sampling

## Calculating Derivatives from Random Forests

(Edits to repair smudges, 2020-06-28, about 0945 EDT. Closing comment, 2020-06-30, 1450 EDT.) There are lots of ways of learning about mathematical constructs, even about actual machines. One way is to push them into a place of improbable application, asking … Continue reading

## COVID-19 statistics, a *caveat* : Sources of data matter

There are a number of sources of COVID-19-related demographics, cases, deaths, numbers testing positive, numbers recovered, and numbers testing negative available. Many of these are not consistent with one another. One could hope at least rates would be consistent, but … Continue reading

## Reanalysis of business visits from deployments of a mobile phone app

This reports a reanalysis of data from the deployment of a mobile phone app, as reported in: M. Yauck, L.-P. Rivest, G. Rothman, “Capture-recapture methods for data on the activation of applications on mobile phones“, Journal of the American Statistical … Continue reading

## “Ten Fatal Flaws in Data Analysis” (Charles Kufs)

Professor Kufs has a fun book, Stats with Cats, and a blog. He also has a blog post tiled “Ten Fatal Flaws in Data Analysis” which, in general, I like. But the presentation has some shortcomings, too, which I note … Continue reading

## On bag bans and sampling plans

Plastic bag bans are all the rage. It’s not the purpose of this post to take a position on the matter. Before you do, however, I’d recommend checking out this: and especially this: (Note: My lovely wife, Claire, presents this … Continue reading

## Sampling: Rejection, Reservoir, and Slice

An article by Suilou Huang for catatrophe modeler AIR-WorldWide of Boston about rejection sampling in CAT modeling got me thinking about pulling together some notes about sampling algorithms of various kinds. There are, of course, books written about this subject, … Continue reading

## Senn’s `… never having to say you are certain’ guest post from Mayo’s blog

via S. Senn: Being a statistician means never having to say you are certain (Guest Post) See also: E. Cai’s blog post “Applied Statistics Lesson of the Day – The Matched Pairs Experimental Design”, from February 2014 A. Deaton, N. … Continue reading

## Eli on “Tom [Karl]’s trick and experimental design“

A very fine post at Eli’s blog for students of statistics, meteorology, and climate (like myself) titled: Tom’s trick and experimental design Excerpt: This and the graph from Menne at the top shows that Karl’s trick is working. Although we … Continue reading

## “Bigger Isn’t Always Better When It Comes to Data”: Barry Nussbaum

The President’s Corner in the May 2017 issue of Amstat News, the monthly newsletter of the American Statistical Association (“ASA”), features the interesting exposition by environmental statistician and President of the ASA, Barry Nussbaum, called “Bigger isn’t always better when … Continue reading

## David Spiegelhalter on `how to spot a dodgy statistic’

In this political season, it’s useful to brush up on rhetorical skills, particularly ones involving numbers and statistics, or what John Allen Paulos called numeracy. Professor David Spiegelhalter has written a guide to some of these tricks. Read the whole … Continue reading

## On Smart Data

One of the things I find surprising, if not astonishing, is that in the rush to embrace Big Data, a lot of learning and statistical technique has been left apparently discarded along the way. I’m hardly the first to point … Continue reading

## “Catching long tail distribution” (Ted Dunning)

One of the best presentations on what can happen if someone takes a naive approach to network data. It also highlights what is, to my mind, the greatly underappreciated t-distribution, which is typically only used in connection with frequentist Student … Continue reading

## Going down to the Southern Ocean, by Earle Wilson (on the Scripps R/V Roger Revelle)

(Click on picture to see a larger image, and use your browser Back button to return to reading.) Getting steady data from the Earth’s oceans demands commitment and not a little courage. I could never do what these oceanographers do, … Continue reading

## Ah, Hypergeometric!

(“Ah, Hypergeometric!” To be said with the same resignation and acceptance as in “I’ll burn my books–Ah, Mephistopheles!” from Faust.)😉 Dr John Cook, eminent all ’round statistician (with a specialty in biostatistics) and statistical consultant, took up a comment I … Continue reading