## Evaluating Probability Models

A video introduction on how to evaluate probability models using the statistical deviance. (link)

## The Purpose of our Data Science Chalk Talk Series

I’d like to share an introduction to my data science chalk talk series (video link, series link)

## Classification as Censored Regression

I have a new short video lecture to share: “Classification as Censored Regression.”

## What is Statistics?

I recently shared a bit of the history of The Science of Data Analysis. I thought I would follow that up with a quick chalk talk titled “What is Statistics?” (link)

## The Science of Data Analysis

I am re-reading from the great statistician John W. Tukey’s paper: Tukey, John W. “The Future of Data Analysis.” Ann. Math. Statist. 33 (1962), no. 1, pp. 1–67. doi:10.1214/aoms/1177704711. https://projecteuclid.org/euclid.aoms/1177704711 I’ve taken the liberty of pulling out some quotes that are very relevant to the usual “data science is not […]

## New Free Video Lecture: Estimating the Odds with Bayes’ Law

I am excited to share my new free video lecture: Estimating the Odds with Bayes’ Law. (link)

## New Free Mini-Lecture: Simpson’s Paradox in A/B Testing

I’d like to share a new fee mini-lecture on avoiding Simpson’s Paradox when analyzing A/B test results.

## A Single Parameter Family Characterizing Probability Model Performance

Introduction We’ve been writing on the distribution density shapes expected for probability models in ROC (receiver operator characteristic) plots, double density plots, and normal/logit-normal densities frameworks. I thought I would re-approach the issue with a specific family of examples.

## An Example of a Calibrated Model that is not Fully Calibrated

Our group has written a lot on calibration of models and even conditional calibration of models. In our last note we mentioned the possibility of “fully calibrated models.” This note is an example of a probability model that is calibrated in the traditional sense, but not fully calibrated in a […]