I'm building taloswork, a desktop AI workspace. One of our published research runs collected 180 eBay listings for “OLED panel.” The results included laptop replacement screens and tiny embedded OLED modules. In that sample, their median prices were RMB 1,508 and RMB 33.10 respectively. A single average could be mathematically correct and still be the wrong comparison. The report separated those product types and compared prices within the groups. Matching a search term wasn't enough to make two listings competitors. For a product decision, I'd want to see a few actual listings from each group, plus what was left out, before trusting the headline number. Otherwise it's very easy to get a tidy table for a market that doesn't really exist as one category. If you've used listing data for a product decision, what did you find in the rows that made you change the comparison?   submitted by   /u/Live_Hedgehog8785 [link]   [comments]