No, that shouldn't be the case. Unless the planet is warming, we shouldn't constantly be setting new highs. I'm not sure what the trickery is here.
For Los Angeles (my city) the data goes back 145 days a year. If temperature has not been increasing (and therefore each individual day follows an identical distribution) that would still mean that 1/145 days we'd see a record high in Los Angeles - so about two highs a year. Highs in Los Angeles are obviously going to be correlated to highs in San Diego - but not exactly because of cloud cover and stuff so its reasonable to think that even if we were in a boring unchanging climate we'd still see hundreds if not thousands of records set every day across the country.
With a normal distribution you wouldn't expect even one record to be set every year. The expected case would be zero records in a year.
A record in that data is defined as a particular high in a particular city on a particular day. The data goes back 145 years in Los Angeles.
So if it is the hottest May 24th ever that would be a record.
If everyday followed an identical and uncorrelated distribution then we would expect that 1/145 days would be record.
In your IQ example imagine if you had a school with 365 separate classrooms with 144 students in each one. A new 145th student then enters each classroom. The chance that the new student is the one with the highest IQ is 1/145. So in the universe of the 365 classrooms you'd expect 2 new IQ records to be set.
Let me ask this, why in your school with 365 classrooms would you expect 2 new IQ records to be set with 145 new students. With a normal distribution, you'd expect all of those new students to be within one standard deviation of 100.
> The chance that the new student is the one with the highest IQ is 1/145
Why do you think this is the case?
Here is some python code you can run. You can change around the distribution however you want and you'd get the same exact results. The chance that a particular record is the highest in a set of IID variables is not dependent on the distribution itself.
from numpy import random
RecordsSet=0
TotalSimulations=365000
for i in range(0,TotalSimulations):
ClassRoomSample = random.normal(100, 15, 144)
RecordIQ = max(ClassRoomSample)
NewStudentIQ = random.normal(100, 15, 1)
if NewStudentIQ>RecordIQ:
RecordsSet=RecordsSet+1
print(100*float(1)/145)
print(100*float(RecordsSet)/TotalSimulations)
However, we should be somewhat regularly setting new highs just because we have on the order of just 200 years of data (so just 200 data points for each daily high); if the planet were not warming, and the high for any given day will be normally distributed around some average for that day, then as time marches on the odds of getting an extreme outlier increases. Some cities on some days will have a high that is very improbable, while others will not have gotten "lucky" yet.