Because you dont actually understand what random is.
I didnt bring up police line ups that’s someone else in this thread. Im just happy to point out how those are essentially random selection as well.
My original point was that the OP was ambiguous on the selection process used for the population. thusly assuming a random selection process from the overall popullation is just as valid as assuming 1 worm farmer + 9 randoms.
Now: randomness.
Given a random population of N entities, assume you want a sample of M where M < N and you decide to filter by Y to get X sample.
X is still random despite the filtering. all you’ve done is biased the N random population towards Y. but unless you remove the initial randomness (somehow… which is almost impossible btw). this is why so much of science is predicated on collecting large datasets because we have to make sure enough of the data has the attributes we want that it’ll show up for study once we apply our faulty filtering mechanisms. and its why we spend so much effort creating better and better filters. its also why algorithm that leverage randomness are so powerful, because they match the reality of the problems being solved.
Using the line up as an example:
witness says the individual they saw had a long wide nose and a blue shirt.
reality: The detective has very different definition of what a wide/long nose is than the witness.
applying those two faulty filters to population N is still going to result in a random population because the initial candidate selection before applying those two (faulty) filters is random.
Because you dont actually understand what random is.
I didnt bring up police line ups that’s someone else in this thread. Im just happy to point out how those are essentially random selection as well.
My original point was that the OP was ambiguous on the selection process used for the population. thusly assuming a random selection process from the overall popullation is just as valid as assuming 1 worm farmer + 9 randoms.
Now: randomness.
Given a random population of N entities, assume you want a sample of M where M < N and you decide to filter by Y to get X sample.
X is still random despite the filtering. all you’ve done is biased the N random population towards Y. but unless you remove the initial randomness (somehow… which is almost impossible btw). this is why so much of science is predicated on collecting large datasets because we have to make sure enough of the data has the attributes we want that it’ll show up for study once we apply our faulty filtering mechanisms. and its why we spend so much effort creating better and better filters. its also why algorithm that leverage randomness are so powerful, because they match the reality of the problems being solved.
Using the line up as an example:
applying those two faulty filters to population N is still going to result in a random population because the initial candidate selection before applying those two (faulty) filters is random.
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no, i havent. and in fact anyone asserting this is absolutely moronic and has no idea what they are talking about for reasons I’ve already mentioned.
im not doubling down you dunces just dont know what you’re talking about. again as I’ve stated repeatedly.
lets start over so you can understand:
you have:
you are asked:
facts:
chucklefucks:
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So how many people need to explain the same thing to you before you realize that you’re wrong? There’s already 4.
There’s no one here supporting you, I wonder why that is?
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;)
Well done
I’ve read it multiple times, the only one who needs a clue is you. Take the hint dude, seriously.