Hahaha, ok so it's an avalanche of spam and very little ham it sounds like. That sounds better than the inverse to me actually 😂

My best tip is to be very ruthless in having rules for email that automatically gets cleaned out of the inbox, e.g. automatic emails from systems, stuff that gets sent out as FYI, weekly XYZ digests etc. They all go into their own folders and I look at them if/when I feel like it. Maybe never.

In my old job I would get around 20-30 emails per day that required me specifically to react to them (so, no automated emails, no mailing list stuff). In my new job, so far ✨almost nothing✨. But I think email volume is likely a function of your tenure and/or network centrality in an organisation.

Wow, thank you so much @thoughtfulnz.bsky.social ! That's really pretty & so nice of you to make up a toy example! 🤩🤩🤩

Ooh, that's a thought! Thanks! I think it's good for the audience to have a sense of how prevalent true vs faulty answers are, but that doesn't necessarily need to be in the same plot as the comparisons 🤔

Thanks Libby! Something like this is what I'm leaning towards but with four sections (so that I can show false positives and false negatives separately, they have different business implications)

Fair point! Right now I'm just going : right answer is the one a human gave + false positives and false negatives have different cost (false negatives are much more costly than false positives)

Oh hey thanks, I hadn't really thought of radar plot for this! That's definitely worth considering!

I think the problem is that data science is >10% thinking and you can't really externalize the thinking, just the execution. And even for execution you need to split in suitable sized chunks that you quality control yourself.

I’m really loving it for making and tweaking plots where I kind of know the plotting library but not well enough to remember all the functions and params by heart. @hadley.nz had a wonderful demo of this at the Posit conf earlier this month, it might be on youtube soon!

Maybe sometime down the line! Right I have a number of evals coming from many different models & frameworks and need to figure out how to best visualize them for our dev team.

My mentor once said: strongest results are the ones where you don't even need stats to figure out if there was an effect, you just plot the raw data and look at it with your two human eyes. If it's obvious then, it's a ding-dang strong effect.

Hi! Just wanted to let you know that there seems to be something wrong with the link - I get an 404 page when trying to open it and cant find the blog even in the blog tab.

Ok that's a very good point! Could it be that since knowing how to do something in python is such a small part of a data scientist expertise it's just not something that is a strong part of our identity? Like, I wear clothes every day but I would never call myself "a real fashionista." Cause 🤷‍♀️