"Possibility of shiny rates being weighted RNG - a research plea"

#PokemonGO: Everything in the game involves RNG, however we already know, thanks to a lot of tireless research, that some (most) things are weighted RNG. Mostly on purpose, but it's entirely possible for something to end up weighted as coding mistake. For example what egg you get is defined by RNG, but you don't have an equal chance to get every egg, this is a key feature of the game - the RNG rolls a number, but what that random number means is not random, most likely there is some kind of look up table.We know shiny is RNG, and from some recent analysis it may be based on a second seed & pseudo-RNG process from the rest of the pokemon's attributes (height, weight, iv).What I suspect based on results from ponyta/cubone and current psyduck events is that there is something odd going on. The net rates are averaged across tens or hundreds of trainers, we'd always end up with a single average number, that's the way averages work. Furthermore we'd have lots of people who were 'lucky' or 'unlucky', that's the way probability works. But the distributions instinctively feel off to me. There are far more people getting high checks and no shinies & low checks but multiple shinies than i feel we'd expect if everyone was operating under the same probability for these events.I suspect that something either in the way the seed for the pRNG is generated, or the way the look up table is working is resulting is different probabilities for different trainers, in different places at different times. That's not to say it's necessarily bias against any accounts, just combinations of account/place/time may change the resulting probability.This is a fairly easy thing to test for with existing data that is being gathered. All that would be needed is to separate all individuals into pools of the number of shinies they got over the event, then plot histograms of the number of pokemon they checked in total. The results should be perfect normal distributions with predictable mean and standard deviation. Just as if we'd get if we got lots of people to roll a dice n times and tracked how many 6's they got.I think these middling shiny rate events (1/50, 1/75 or whatever it is) would be really important to analyse as regular shiny rates require much greater volume of checks, so need much more data, and community day rates may be too high so masking a lot of variance.If I'm right and these histograms don't show a normal distribution with predicted means & SD it would be hugely important in terms of our understanding of how shinies are determined (or not). Equally, nice predictable distributions would show its a perfect pRNG system.TL:DR existing shiny data collection could be used to find out if shiny rates are same for everyone via /r/TheSilphRoad http://bit.ly/2MGvolj