http://ssp.impulsetrain.com/celebrities.html
I still love me a good conspiracy theory, but clustering of random (poisson) events is much more likely than you'd expect.
We are talking about a period of a few days here and about only a handful of services that tout 99.999(9)% uptime. I'm no mathematician but I don't think it's a great comparison.
Now the human psychology part that this doesn't cover is that typically when you have two or three A-listers die, well, then you start seeing all the B- C- and D-listers that also died in the same period that you would have otherwise ignored.
I think that would just end this discussion. I don't know how to calculate that, but my intuition says the resulting chance is low.
import random
service_providers = 20
servers_threshold = 5
up_time_threshold = 0.999**7 # prob down in week
years = 20
occurrences = 0
# run sim 10,000 times
for i in range(0, 10000):
servers_down = False
# simulate weeks in 20 years
for j in range(1, 52*years):
how_many_down_this_week = 0
# run up times for service providers
for s in range(0, service_providers):
up_time = random.random()
if up_time > up_time_threshold:
how_many_down_this_week += 1
# did they go down?
if how_many_down_this_week > servers_threshold:
servers_down = True
if servers_down == True:
occurrences += 1
print(occurrences)Google's own status page[1] list near 100 incidents in the past 365 days, only one of which ended on HN frontpage.
[0] https://en.wikipedia.org/wiki/Timeline_of_Amazon_Web_Service...
https://news.ycombinator.com/item?id=20077421
https://news.ycombinator.com/item?id=20338263
Both of these AWS outages made it to the HN frontpage (and neither are listed in that AWS timeline):
Same thing with hurricanes, when a bad one happens in the US they are more likely to document other minor hurricanes in the Caribbean and Latin America. But otherwise plenty of hurricanes hit those areas and it doesn't make major headlines.
This is more like 6-8, not e. It's definitely odd.
One non-conspiracy explanation I can imagine is that maybe all these big providers have a bunch of hidden dependencies on each other.
My point is simply that our intuition of randomness biases us to see meaning in clusters when there is none in the first place. In other words, we need to intentionally shift our prior on how unlikely this event is. Yes, it's still unlikely, but it's not as unlikely as you would think.