One should check for one more fallacy: what if the same great authors always post at the same time and the author is the deciding factor for number of likes and not time?
Two methods come to mind:
Divide the likes of each post by the median number of likes of the author.
just check if like count is related to userId over the whole data set.
I think this kind of fallacy can also be caught by measuring the median number of like across the day instead of mean. I did it and did not find that much difference in the results.
I'll try your point 1, haven't thought of this one though.
And I plan to make an article about "famous" poster and their stat that will deal about point 2.
If we assume the opposite of "some people are great writers", namely "most people are bad writers", then there might be a situation where the majority make not good articles that aren't affected by time of day. In such a scenario using the median would be less telling.
That said, most posts here are pretty well-made, so that shouldn't be a problem. If it were the case, then the median would br pretty much even across all times of day, so if you didn't see much difference, then that disproves it.
I've been a professional C, Perl, PHP and Python developer.
I'm an ex-sysadmin from the late 20th century.
These days I do more Javascript and CSS and whatnot, and promote UX and accessibility.
If we think that "great authors" post at the same time then, even if we cater for numbers of users, we're basically assigning qualities to longitudes. One uncausal correlation from that would tell us we'd get more likes if we moved to a different country.
I think this is a whole lot like the rocket equation. The more people try to target "best times", the more they'll skew the data away from themselves.
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One should check for one more fallacy: what if the same great authors always post at the same time and the author is the deciding factor for number of likes and not time?
Two methods come to mind:
Yes, you are totally right.
I think this kind of fallacy can also be caught by measuring the median number of like across the day instead of mean. I did it and did not find that much difference in the results.
I'll try your point 1, haven't thought of this one though.
And I plan to make an article about "famous" poster and their stat that will deal about point 2.
If we assume the opposite of "some people are great writers", namely "most people are bad writers", then there might be a situation where the majority make not good articles that aren't affected by time of day. In such a scenario using the median would be less telling.
That said, most posts here are pretty well-made, so that shouldn't be a problem. If it were the case, then the median would br pretty much even across all times of day, so if you didn't see much difference, then that disproves it.
Nice catch there...
If we think that "great authors" post at the same time then, even if we cater for numbers of users, we're basically assigning qualities to longitudes. One uncausal correlation from that would tell us we'd get more likes if we moved to a different country.
I think this is a whole lot like the rocket equation. The more people try to target "best times", the more they'll skew the data away from themselves.