Data Science on Polimath

Corporate empathy about AI

srh@haptonstahl.org (Stephen Haptonstahl, Ph.D.) — Thu, 28 May 2026 00:00:00 -0400

A while back I gave my first talk to a general audience about AI. The presentation went well, and it was generally very well received. Then came the Q&A.

The person who spoke first was nearly in tears. They went on at length about the evils of AI and how they would never use it. They said AI is taking jobs (true) and that all the jobs it takes can be done better by humans (not true, in many cases). The speaker used up the time scheduled for questions. They ended, less with a question and more with a challenge to rebut.

Good practices: Naming Things

srh@haptonstahl.org (Stephen Haptonstahl, Ph.D.) — Wed, 07 Jan 2026 00:00:00 -0400

There are only two hard things in Computer Science: cache invalidation and naming things.

Phil Karlton

Bad names create confusion. Good names:

Provide clarity to consumers about what to find inside by telling us why we have the named thing.
Provide guidance to producers about what should and should not go inside, and tell us how we create the named thing.
Are easy to use: concise, memorable, easy-to-spell, not random or disconnected from how and why we have the named thing.
Avoid “collisions” = repeating a name of something else that is not distinguishable from context.

This is not a complete guide to naming, but rather a compilation of good practices¹ that generalize what I have found useful. We’ll discuss:

Get someone else to do it

srh@haptonstahl.org (Stephen Haptonstahl, Ph.D.) — Sat, 08 Nov 2025 00:00:00 -0400

The easiest way to get something done is to get someone else to do it.

Stephen Haptonstahl

Thanksgiving dinner
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Dad is about to put the turkey in the oven. There’s a lot of food yet to prepare. Three guests wander into the kitchen and ask how they can help. The last thing Dad needs is a distraction!

How I lead (data) teams: principles

srh@haptonstahl.org (Stephen Haptonstahl, Ph.D.) — Sun, 13 Jul 2025 00:00:00 -0400

tl;dr
#

North star: Happier teams deliver more.
How I sail that course:
- Remove demotivating emotional shielding, using empathy and vulnerability to create a safe space.
- Motivate by providing purpose, autonomy, and the chance to develop mastery.
- Don’t control: trust.

Management is allocating resources. Leadership is influencing people toward goals.

How sure are you? Confidence v. Prediction intervals

srh@haptonstahl.org (Stephen Haptonstahl, Ph.D.) — Thu, 03 Jul 2025 00:00:00 -0400

Two common uses for statistical (or machine learning) models are:

Something happened. Why?
Something will happen. What?

Spoiler Alert
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When reporting results, tell your audience how certain you are. This is essential context.
“Why?”: When presenting parameters (drivers, slopes, causal effects, and the like) use something like a confidence interval, Bayesian credible interval, or bootstrapping the data.
“What?”: When presenting predictions, use a conformal prediction interval, Bayesian posterior predictive interval, or the like. Forecasts must use these.

“Why?” = inference about drivers
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When we ask “why?” we are trying to make an inference about how the data was generated. Specifically, inferences about the parameters of the model, which can be thought of as measures of the relationships between features and the target.

One number doesn't do it: Context matters

srh@haptonstahl.org (Stephen Haptonstahl, Ph.D.) — Sun, 22 Jun 2025 00:00:00 -0400

“Our net promoter score for the new version is 42.”

Okay. So what? Is 42 good or bad? Should we continue rolling out the new version, or should we roll back to the previous version?

Not everything valuable is measurable

srh@haptonstahl.org (Stephen Haptonstahl, Ph.D.) — Sun, 01 Jun 2025 00:00:00 -0400

I wrote before about types of value generated by data science projects. What happens if you can’t report value in dollars?

James Q. WIlson classified bureaucratic agencies based on whether their inputs and outputs were measurable.

The business value of a data science project

srh@haptonstahl.org (Stephen Haptonstahl, Ph.D.) — Mon, 26 May 2025 00:00:00 -0400

Data science should deliver business value.

When considering whether to invest in a data science project, itʼs useful to be clear about the kinds of value the project will bring.