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John855.
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The blackpill hypothesis (BK) predicts that men will be rejected, on average (no universal claims) because of their looks. The ''bluepill'' (BE) hypothesis predicts on average (no universal claims) that men will be rejected because of confidence or personality.
The prior probability of both hypotheses lets give a equal ~50%
P (BE|H) = 0.5 P (BK|H) = 0.5
The next question is going too be, what is the probability of A (Men on average being rejected on dating apps because of their looks) under hypothesis BE, versus hypothesis BK.
A=“the observed dating-app data show men’s rejection is driven more by their looks than by confidence/personality.”
BK Hypothesis: Looks predict rejection.
BE Hypothesis: Personality predicts rejection.
Now we ask, under BK what is the likelihood of A, and under BE is what is the likelihood of A?
Because A matches the predictions of BK, and A does not correspond too the predictions of BE, the evidence favours BK. If BE predicts women on average will reject / accept a man because of his looks, the probabilty of A occuring would be low, now one may say, it isnt low because A does not represent women on average. The problem is other data appears too show that women within what we consider too be average are the main people on dating apps. If A represents women on average, and BK's predictions about A comes true, the evidence favours BK over BA.
Thus, if A represents the majority of the womens intentions, and BK's predictions about it comes true, and we have established the evidence favours BK, what is in question is how much does the evidence favour BK, does it favour it 51/49 or something like 95/5?
Because of how strong the claim A is actually occured per the data, it gives a much higher favouring towards the BK over the BP. It would take me longer too generate a exact percentage, but we can be highly confident the favouring is not something like 51/49 because of how much A favours BK, considering how much the prediction BK occured.
Also, A is not the blackpill hypothesis restated, it is what the raw data appears too say.
So, if anyone could gather strong data representing women on average, and whether BK's predictions match that data, then form a probability, id be interested.
The prior probability of both hypotheses lets give a equal ~50%
P (BE|H) = 0.5 P (BK|H) = 0.5
The next question is going too be, what is the probability of A (Men on average being rejected on dating apps because of their looks) under hypothesis BE, versus hypothesis BK.
A=“the observed dating-app data show men’s rejection is driven more by their looks than by confidence/personality.”
BK Hypothesis: Looks predict rejection.
BE Hypothesis: Personality predicts rejection.
Now we ask, under BK what is the likelihood of A, and under BE is what is the likelihood of A?
Because A matches the predictions of BK, and A does not correspond too the predictions of BE, the evidence favours BK. If BE predicts women on average will reject / accept a man because of his looks, the probabilty of A occuring would be low, now one may say, it isnt low because A does not represent women on average. The problem is other data appears too show that women within what we consider too be average are the main people on dating apps. If A represents women on average, and BK's predictions about A comes true, the evidence favours BK over BA.
Thus, if A represents the majority of the womens intentions, and BK's predictions about it comes true, and we have established the evidence favours BK, what is in question is how much does the evidence favour BK, does it favour it 51/49 or something like 95/5?
Because of how strong the claim A is actually occured per the data, it gives a much higher favouring towards the BK over the BP. It would take me longer too generate a exact percentage, but we can be highly confident the favouring is not something like 51/49 because of how much A favours BK, considering how much the prediction BK occured.
Also, A is not the blackpill hypothesis restated, it is what the raw data appears too say.
So, if anyone could gather strong data representing women on average, and whether BK's predictions match that data, then form a probability, id be interested.
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