Earlier quoted context omitted.
Sometimes feature requests are actually bugs and can be illustrative of one not properly understanding design. But I think it is important how user feature requests are interpreted. They have a frustration that you might not be aware of but they aren't aware of all the code and constraints. It can even be in design, which is still important. Very often there is a way to resolve a feature request that is not what the…
> Sometimes feature requests are actually bugs You can have two missing features that add up to a bug in total. For example, I worked with two cloud products from the same vendor where a missing back-end HTTP feature of the CDN product interacted with a missing HTTP front-end feature of the PaaS service such that the two products that have a "natural fit" together couldn't actually be used in combination. This made m…
I don't think this is off topic at all. I think is is explicitly on topic, at least the the underlying one. Not just statistics are hard, but it's hard to measure things and even harder to determine causality. Which is often the underlying goal of statistics and data science. To find out why things happen. Measurements are incredibly difficult and people often think they are simple. The problem is that whatever you're measuring is actually always a proxy and has uncertainty. Often uncertainty you won't know about if you don't have a good understanding of what the metric means. You'll always reap the rewards when putting in the hard work to do this, but unfortunately if you don't it can take time before the seams start to crack. I think this asymmetry is often why people get sloppy.