As I’ve written about before, as a data scientist supporting a product or marketing team with A/B testing, the job is communication – helping to translate between business requirements and what we can learn from statistics. I (and many, many others) have found that there is a lot of value in having a …

Read More

The other day, I was reading a post by Venkatash Rao (thousands of words of under-edited brilliance, as usual), and was struck by this note about the complexity of climate solutions: I tend to take as an article of faith the systems science rule of thumb that the complexity of solutions generally matches the …

Read More

There’s been an immense amount of discussion about Large Language Models (LLMs) such as ChatGPT over the last year, of course. Some of that discussion has been whether they are intelligent, conscious, or on the path to Artificial General Intelligence. I’m particularly interested in the “consciousness” …

Read More

Recently I wrote a blog post that mentioned “Superiority” as a type of A/B test decision. In this post I want to talk about all five types of A/B test decision that I think are relevant. This is an adaptation and extension of a talk I gave last year at the Quant UX conference (it’s a great event, you should check it …

Read More

Recently, tech-journalism site The Markup ran a long, detailed, critical investigation of a predictive machine learning model used by the State of Wisconsin to identify public school students at risk of not graduating. I mostly agree with the conclusions of the piece – the system appears not to be fit for purpose …

Read More

The “best practice”, when evaluating the results of an online controlled experiment (A/B test), is to use classical statistical tests, proceeding with a change if (and only if) the result of the test includes a p value of less than 0.05. But, the American Statistical Association (ASA) said in a prominent …

Read More

A/B testing is a tool for supporting decision-making in business, and so in addition to getting the statistics right, it’s really important to communicate well with the non-statisticians who will have the final say on the go/no-go decision. Most A/B tests in practice are testing ratios, conversion rates of various …

Read More

I recently read Will Larson’s excellent book Staff Engineer: Leadership beyond the management track. Larson covers the individual contributor (IC, not management) roles that software engineers fill after they are promoted past Senior Software Engineer, with titles like Staff and Principal …

Read More

Suppose you’re a data scientist at an e-commerce web site that sells shoes, responsible for supporting A/B tests. Many A/B tests are easy, and there are a number of companies that sell tools that make the easy cases as simple as clicking a few buttons and looking at pretty graphs. But A/B tests can get statistically …

Read More

Just a quick post here to note a few professional accomplishments: I just added a new publication to my vita – a peer-reviewed conference proceeedings article about abstractions for building repeated, related versions of similar predictive models. Check out some longer thoughts on Medium, or read the full …

Read More