If you're doing any serious analysis of UAP data, the first thing you need to control for is observer bias. Here's what it actually looks like in the numbers. Weekend effect: 45.3% of sightings occur Friday–Sunday. Expected if random: 42.9% (3/7 days). That's a χ²(1) = 493.0 — highly significant overrepresentation of weekend reports. People are outside more on weekends. They have phones. They're not at work. Summer peak: 30.6% of sightings fall in June–August. Expected if uniform: 25%. Peak month is July with 22,496 sightings — the 4th of July effect is real and enormous when you look at the raw data. Why does this matter? Any hypothesis you test against UAP data needs to control for these distributions. If you find "more sightings near X on summer weekends," you've found observer density, not signal. The geomagnetic storm analysis I posted earlier used era-stratified controls partly for this reason — storm frequency isn't evenly distributed across seasons, so naively comparing storm vs. calm days without stratification would absorb some of the seasonal bias. The fireball coincidence result survived controls for both day-of-week and season. That makes it more interesting, not less. None of this means the data is useless — it means you need to handle it correctly. The observer bias patterns are themselves quantified and available to cross-reference in any analysis you run. Data and methodology: uapmonitor.org/research If you're doing your own analysis and want the raw dataset (CSV/JSON export): it's on the platform, filter by whatever criteria you need. https://preview.redd.it/gwrvtt4ufhsg1.png?width=1738&format=png&auto=webp&s=bb9acb777316d1f89321e6742203cb47e45524b1 submitted by /u/moe_sidani [link] [comments]