Same here. Can’t count the number of times I’ve had to come in and say “hold on, you built an entire report with conclusions and recommendations but didn’t stop to say hmm this data looks weird and dig into validation?” “We assumed the data was right and that it must be xyz…”
A corollary if this that is my personal pet peeve is attributing everything you can’t explain to “seasonality” , that is such a crutch. If you can’t explain it then just say that. There is a better than not chance it is noise anyway.
> A corollary if this that is my personal pet peeve is attributing everything you can’t explain to “seasonality” , that is such a crutch. If you can’t explain it then just say that. There is a better than not chance it is noise anyway.
Very early in my career, I discovered python's FFT libraries, and thought I was being clever when plugging in satellite data and getting a strong signal.
I share this experience of people often just performing the steps without thinking about the meaning behind them. In data analysis and software development.
My conclusion so far has been "well they are not doing their job properly".
I assume that's the kinds of jobs LLM's can replace: People you don't want on your payroll anyway
Presented as a literal thing, but is really figurative. What I mean is, often you don’t have the data to actually plot/PCA/whatever technique the seasonality, maybe you only have 2 years of data for example. But it dips in October and you have no idea why so you just say “Q4 tends to be a low season for this product” or something equally dubious, with no further analysis or hope of same
Just because something happens on a yearly cadence doesn't mean that "seasonality" is a good reasoning. It's just restating that it happens on a yearly cadence, it doesn't actually explain why it happens.
Is there a blog post that just focus on the gorilla test that I can share with my team? I’m not even interested in the LLM part