One of the dirty secrets of a lot of these "code adjacent" areas is that they have very little testing.

If a data science team modeled something incorrectly in their simulation, who's gonna catch it? Usually nobody. At least not until it's too late. Will you say "this doesn't look plausible" about the output? Or maybe you'll be too worried about getting chided for "not being data driven" enough.

If an exec tells an intern or temp to vibecode that thing instead, then you definitely won't have any checkpoints in the process to make sure the human-language prompt describing process was properly turned into the right simulation. But unlike in coding, you don't have a user-facing product that someone can click around in, or send requests to, and verify. Is there a test suite for the giant excel doc? I'm assuming no, maybe I'm wrong.

It feels like it's going to be very hard for anyone working in areas with less black-and-white verifiability or correctness like that sort of financial modeling.

This has had tremendous real world consequences. The European austerity wave of the early 2010s was largely downstream of an excel spreadsheet errors that changed the result of a major study on the impact of debt/gdp.

https://www.newscientist.com/article/dn23448-how-to-stop-exc...

This is a pet peeve of mine at work.

Any and I mean any statistic someone throws at me I will try and dig in. And if I'm able to, I will usually find that something is very wrong somewhere. As in, the underlying data is usually just wrong, invalidating the whole thing or the data is reasonably sound but the person doing the analysis is making incorrect assumptions about parts of the data and then drawing incorrect conclusions.

> If a data science team modeled something incorrectly in their simulation, who's gonna catch it? Usually nobody. At least not until it's too late.

Back in my data scientist days I used to push for testing and verification of models. Got told off for reducing the teams speed. If the model works well enough to get money in, and the managers that make the final calls do not understand the implications of being wrong, this would be the majority of cases.

> If a data science team modeled something incorrectly in their simulation, who's gonna catch it? Usually nobody. At least not until it's too late. Will you say "this doesn't look plausible" about the output?

The local statistics office here recently presented salary statistics claiming that teachers' salaries had unexpectedly increased by 50%. All the press releases went out, and it was only questions raised by the public that forced the statistics office to review and correct the data.

I did a fair about of data analysis and deciding when or if my report was correct was a huge adrenaline rush.

A huge test for me was to have people review my analyses and poke holes. You feel good when your last 50 reports didn’t have a single thing anyone could point out.

I’ve been seeing a lot of people try to build analyses with AI who haven’t been burned with the “just because it sounds correct doesn’t mean it’s right” dilemma who haven’t realized what it takes before you can stamp your name on an analysis.