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Joined 1 year ago
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Cake day: July 20th, 2023

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  • The bad flooding is because of how steep those areas are. Down in Florida, water takes a while to make its way into the creeks, streams, and rivers. The areas with the most extreme flooding is because entire mountainsides worth of rainfall drains in the span of a couple days.

    Eastern TN and western NC also had a ton of rainfall from thunderstorms just before Helene so the ground was already saturated. For reference check out this rain gauge in Asheville.

    https://waterdata.usgs.gov/monitoring-location/353312082355545/#parameterCode=00045&period=P7D&showMedian=false

    The rain from Helene didn’t really get to Western NC until the early hours of Friday the 27th. That gauge was already at 10 inches by that time. So really the storm before Helene brought more rain than Helene did. Either the thunderstorm or Helene alone would have been moderate, but manageable flash floods, but the two back to back was insane.












  • I’m not saying normalization is a bad strategy, just that it, like any other processing technique comes with limitations and requires extra attention to avoid incorrect conclusions when interpreting the results.

    Because relative to the population density, there were 100 times as many sightings. Or what am I missing.

    If you were to attempt to trap and tag bigfoots in both areas, would you end up with 100 times as many angry people in a gorilla suit in the small town? No. You would end up with 1 in both areas. So while the tiny town does technically have 100x the density per capita, each region has only one observable suit wearer.

    Assuming the distribution of gorilla suit wearers is uniform, you would expect approximately 99 tiny towns with no big foot sightings for every 1 town with a sighting. So if you were to sample random small towns, because the map says big foots live near small towns, you would actually see fewer hairy beasts than your peer who decided to sample areas with higher population density.

    If we could have fractional observations, then all this would be a lot more straightforward, but the discrete nature of the subject matter makes the data imherently noisy. Interpreting data involving discrete events is a whole art and usually involves a lot of filtering.


  • Simple normalization does amplify signals in low density areas. If a person in a tiny town of 100 reports a bigfoot sighting and another person in an area with 10,000 population also reports a sighting, then with simple normalization the map would show the area with 100 people having 100 times as many big foot sightings per capita as the area with the population of 10k. Someone casually reading the map would erroneously conclude that the tiny town is a bigfoot hotspot and would in general conclude bigfoot clearly prefers rural areas where they can hide in seclusion. When the reality is that the intense signals are artifacts of the sampling/processing methods and both areas have the same number of fursuit wearers.





  • I’ve had that happen with what i assume was a hand lotion because there was a particular part of the lid that smelled.

    I don’t know why other people are treating you with so much disbelief. This absolutely can happen with people not thinking about how their habits impact what customers are consuming. With how many millions of coffee beverages that are served every day, it shouldn’t surprise anyone that some small portion are handled improperly with poor hygiene. It also shouldn’t be overly surprising if you’ve had it happen multiple times because you likely visit the shops near you. Such an event isn’t random and is the result of someone’s bad habits.