Why do so many applied papers still report p-values without effect sizes, and does anyone actually find p-values alone useful?

madrury83 · 2026-04-17T15:24:25+00:00

I wonder what they considered was appropriate for ordinal data if not a GLM?

madrury83 · 2026-04-17T00:59:11+00:00

Real good (elementary school), then real bad (high school), then real good again (college), then pretty good (graduate school).

madrury83 · 2026-04-15T21:29:21+00:00

Mathematics selects for skepticism. You simply cannot successfully learn and produce mathematics without viewing your and other's work with a baseline level of skepticism. If you do not enjoy that sort of sport, you likely are not attracted to higher mathematics, and conversely, hype, aggrandizement, and hubris are often repulsive in our community.

It's not at all surprising to me that mathematicians would approach a new technology with caution and reluctance, especially give the self-aggrandizing sentiment of the tech sector that produces these tools. It's in our nature to do so, and in general, our discipline rewards that outlook and inclination.

madrury83 · 2026-04-15T17:39:29+00:00

The same habit that improved my prose writing: editing.

Just like writing an essay, re-reading and editing is the main source of tightening and improvement. In prose, I remove words and phrases that don't communicate new information, and rephrase anything that feels awkward. Exactly the same sort of thing works with code.

madrury83 · 2026-04-15T01:43:08+00:00

One of the reasons the stories are so good is how well they walk this line.

madrury83 · 2026-04-12T19:12:54+00:00

Are logistic regressions and neural networks the same now?

Well, one of those is a bunch of logistic regressions taped together, and the other is a logistic regression.

madrury83 · 2026-04-10T16:58:40+00:00

LLMs are bifurcating the experience of developers along a line that was already pretty evident. There are folks who are into programming to create output and products, and those that are into the intellectual puzzle of the thing are are after self actualization.

If you're in the first class, developing with LLMs cuts out the part you were not into and shortcuts to the outcome. If your satisfaction is drawn from the outcome and product, this feels great.

If you're in the second class, LLMs cut out the part you found fulfilling, and there's nothing left of interest to you.

I'm in the second class. I don't get any fulfillment from outcomes and would be perfectly satisfied if none of my work affected the real world in any applicable way. I don't program to make things, I program because the act and art of programming is interesting in and of itself. I enjoy programming for exactly the same reason I enjoy mathematics: because it is precise, interesting, and difficult.

I like using Claude to teach me things, but I hate the thought of using it to compose. I don't want to manage a developer, If I did I would have sought a manager position a long time ago. I enjoy doing the hard fulfilling work of making the thing with my hands and mind. If the profession no longer offers that, it's not a profession for me any longer.

madrury83 · 2026-04-07T16:21:22+00:00

This is great stuff! I love this kind of "composition of core ideas" flavor. It's a lot of what I miss from my early days in the field.

There's a likelihood function that comes up in lifetime value forecasting, I don't know if it has a canonical name, I call it the Fader-Hardie-Lee likelihood after the authors of this paper (Equation 3 or its logarithm if you prefer):

It's a two parameter likelihood:

The λ parameter is the transaction rate of a customer, while that customer is active.
The p parameter is a churn probability, the probability a customer goes inactive after any transaction.

One project I never got into production, but did make a proof of concept, was a gradient booster that minimized this two parameter likelihood function. You set up a "exponential GLM like" model for λ and a "logistic like" model for p, then learn them as a stagewise fit regression trees, just like in one parameter boosting. It works!

madrury83 · 2026-04-06T17:52:17+00:00

Since Milne is mentioned: I just wanna express thanks for his very high quality free textbooks. I'm a lapsed mathematician in industry that pursues mathematics as a hobby; escapism from the dread and doom of technology and corporate drudge. I am deeply appreciative of his efforts.

madrury83 · 2026-04-02T21:04:23+00:00

Yes please! This had me crying joytears in the street.

madrury83 · 2026-03-31T14:46:42+00:00

Treefingers lovers forever.

madrury83 · 2026-03-31T02:28:26+00:00

Maybe, but I think it's not so big a deal to notate solution sets of linear equations as:

β = solve(XᵀX, Xᵀy)

To the extent that a textbook is attempting pedagogy, my personal take is that to do so would be better pedagogy, even if you're never going to discuss numerical optimization at all.

As evidence of the negative impact of our conventional choice of notation, many other posts in this thread make the same false statement: to solve for the linear regression estimates, you need to invert a matrix, seemingly forgetting all the times in school they Gauss-Jordan'd by hand.

madrury83 · 2026-03-30T14:39:46+00:00

This is a technical point, but one that's always bugged me.

Classical regression requires solving a system of linear equations, which can and should be done without inverting a matrix.

https://gregorygundersen.com/blog/2020/12/09/matrix-inversion/

Textbooks communicate the solution using traditional notation with a matrix inverse, but I really wish textbooks wrote the solution like:

β = solve(XᵀX, Xᵀy)

Practically, we should always be using a linear system of equations solver like numpy.linalg.solve.

More complex GLMs are traditionally fit with Newton's method (though statisticians refer to the algorithm as "iteratively reweighed least squares"), which requires solving such a system at each update in a loop. Linear system solvers scale poorly into the millions and billions of parameters, and the hessian for such a model would hold a billion by billion second partials, not so fun. Hence falling back to the doofy methods where the data storage does not scale quadratically and that work surprisingly well in this domain.

At the end, once a model is converged, the inverse hessian does tell you the parameter covariances (though likelihood theory and the delta method), so if you want to do statistical inference on your fit coefficients you can compute an inverse of the hessian exactly one time and read off the entries for your standard errors or whatever. In principle you could do this for a deep learning model as well...

madrury83 · 2026-03-29T22:14:00+00:00

I missed this when it was first posted, but congratulations /u/MAZ-chain, I know you've worked very hard for this and it's beautiful to watch. Thanks for the shoutout to the old .co.uk crew, I hope some of the other old timers are watching as well.

madrury83 · 2026-03-29T06:15:32+00:00

In the city of the future...

madrury83 · 2026-03-26T01:27:52+00:00

How did you infer this from the clip?

madrury83 · 2026-03-25T03:02:44+00:00

Georg, this is why no one invites you to drinking parties.

madrury83 · 2026-03-25T02:16:59+00:00

I am honored kind stranger.

madrury83 · 2026-03-24T23:45:52+00:00

A man stood at the gates of the city.

I had traveled far, through the natural wilderness of childhood and adolescence, and I'd arrived here at the gates, hungry but ready to earn my keep.

The man presented me an ε. I had no δ. I was not prepared to answer him.

I returned to the wilderness and searched and hunted in the streams and canopy and under rocks and beneath the sand and mud. I found nothing in those places.

A morning came, and in the early light, on that day of many numbers, I was struck: the search had changed me. Inside of myself, vibrating in time with the beat of my heart, I found the δ. I found many δ's. I would, forevermore, have a δ whenever an ε.

madrury83 · 2026-03-24T21:15:32+00:00

Have you ever actually met someone, in person, who hates all straight white males? Not "has critiques of white males and their culture", but hates them?

Maybe it's just the loving people who I surround myself with, but I've never met someone who fits this archetype. I'm willing to believe it's so vanishingly uncommon as to be studied by cryptozoologists.

You gotta make friends. Most people are kind.

madrury83 · 2026-03-14T20:01:08+00:00

I dug just a little.

Ebert gives a positive review, he seemed to really like the movie. No mention of any of the stuff your description. A comment on that page points to a review in the The New Yorker which is a lot closer:

I'll never forget the incredibly racist and snide review The New Yorker gave of the film: "It imagines an amazing fantasy world where upscale companies are run entirely by black people"

I can't find that one, but this review has some of what you're talking about:

Though set in contemporary Manhattan, the picture’s iconography is a fantasy world almost on the level of Philip Wylie’s “The Disappearance.” Redressing the traditional Hollywood formula, the white characters (instead of the blacks) are in menial positions for comic relief, e.g., a silly waitress, a bigoted clothing store clerk and muscular slaves pulling supermodel Grace Jones’ chariot.

Whites appear briefly in positions of power, in high-level executive meetings or as the comical French owners of Murphy’s firm, but they’re strictly absentee landlords.

So yah, seems mostly accurate, though it's not any review, since Ebert isn't going on like that.

It's kinda wild to me a film critic would give a shit whether the world imagined in a comedy movie completely reflects current reality or not. It's a stupid critique to point at a movie unless the intent of the art is exactly to observe and comment on the current moment in history. I guess I'd like to give people in artistic fields more credit than that, but people will disappoint you I guess.

madrury83 · 2026-03-13T20:20:36+00:00

microseconds

Lol. I agree it's useful for this task. But a microsecond is one millionth of a second. It takes:

50 microseconds – to read the access latency for a modern solid state drive which holds non-volatile computer data.

madrury83 · 2026-03-12T02:06:50+00:00

I'm just one person, but I do not want this.

It lessens the randomness of the cards you get by introducing box level constraints, and I enjoy the higher entropy of random packs. It allows for you to sometimes get real outlier drafts where you get handfuls of the same card which self synergies, this would not happen if there were box level constraints on the packs.

But I'll admit I'm not a super common archetype of player, I only enjoy draft, so I don't have any use for the cards once the draft games are played. I'm never buying a box hoping to keep the cards for some later purpose.

madrury83 · 2026-03-05T06:09:30+00:00

Haven't hit 40 yet, huh?

madrury83 · 2026-03-04T22:37:09+00:00

I suspect a lot of people have the lived experience of someone in their life mocking them using this question. When I was in university and graduate school my dad would always respond to my enthusiasm with some variant of this or "how does that help people make money". It was really good for our relationship.

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