The author explains why thinking in terms of averages "across a big enough data set" isn't enough.
>Call me crazy, but I don’t give a shit about averages. For a gaussian "normal" process, probabilities say half of your sample will be above and half will be below the average (which is also the median in a gaussian distribution). If we designed cars for the average load they would have to sustain, it means we would kill about half of the customers. Instead, we design cars for the worst foreseeable scenario, add a safety factor on top, and they still kill a fair amount of them, but a lot fewer than in the past. [...]
>As a photographer, I care about robustness of the visual output. Which means, as a designer, designing for the worst possible image and taking numerical metrics with a grain of salt. And that whole WebP hype is unjustified, in this regard. It surely performs well in well chosen examples, no doubt. The question is : what happens when it doesn’t ? I can’t fine-tune the WebP quality for each individual image on my website, that’s time consuming and WordPress doesn’t even allow that. I can’t have a portfolio of pictures with even 25 % posterized backgrounds either, the whole point of a portfolio is to showcase your skills and results, not to take a wild guess on the compression performance of your image backend. Average won’t do, it’s simply not good enough.
>Call me crazy, but I don’t give a shit about averages. For a gaussian "normal" process, probabilities say half of your sample will be above and half will be below the average (which is also the median in a gaussian distribution). If we designed cars for the average load they would have to sustain, it means we would kill about half of the customers. Instead, we design cars for the worst foreseeable scenario, add a safety factor on top, and they still kill a fair amount of them, but a lot fewer than in the past. [...]
>As a photographer, I care about robustness of the visual output. Which means, as a designer, designing for the worst possible image and taking numerical metrics with a grain of salt. And that whole WebP hype is unjustified, in this regard. It surely performs well in well chosen examples, no doubt. The question is : what happens when it doesn’t ? I can’t fine-tune the WebP quality for each individual image on my website, that’s time consuming and WordPress doesn’t even allow that. I can’t have a portfolio of pictures with even 25 % posterized backgrounds either, the whole point of a portfolio is to showcase your skills and results, not to take a wild guess on the compression performance of your image backend. Average won’t do, it’s simply not good enough.