The way I’d go about this is probably a bit different (I’m not familiar with those python functions and I’m probably not totally understanding your approach, but I think you’re probably overcomplicating it and also doing it wrong :D):
I’d have an experiment of let’s say 1000 events with the proposed probabilities of T and F
I’d run the experiment a number N of times and evaluate for each experiment if the number of T was equal or greater than 750. The number of experiments meeting this condition would be S (of success)
The answer to the problem in this experimental approach would be S / N.
I suddenly remember that I took some classes related to computational statistics.
I’m not sure if I understood you, but you seemed to just divide the number of combinations that would be 75% or more of “being right” by the total number of combinations, and take that as the result? if I got you right, I might need to think again if that’s wrong as I initially thought, because given that F and T have the same probability to occur, then you can take that out of the equation, so you might be right too.
Tried this in Excel, but it is not precise enough. Chance gets close to 1 before 750 and eventually is exactly 1, in Excel, probably due to not enough precision:
Personally, I am not a fan of this philosophy of EEAN, but Eliminative Materialism would counter this part of this deduction in retrospect quite effectively. Nevertheless, one can assume and believe many philosophies in part or whole, which is in all aspects difficult to prove. What a cacophony of human life……
Yes, that was what I tried to do. But where I went wrong is I assumed that the likelihood of getting 3/4 was the same as likelihood of getting 750/1000.
If the question was just Pr(getting 3 out of 4), then I would be right (just confirmed this using Python).
import numpy as np
from scipy.stats import binom
k,n,p=3,4,.5
answer=0
for i in range(k,n+1):
answer=answer+binom.pmf(i,n,p)
First, he had some statements from Charles Darwin and Patrician Churchland.
“With me the horrid doubt always arises whether the convictions of man’s mind, which has been developed from the mind of the lower animals, are of any value or at all trustworthy. Would any one trust in the convictions of a monkey’s mind, if there are any convictions in such a mind?” - Darwin
Boiled down to essentials, a nervous system enables the organism to succeed in the four F’s: feeding, fleeing, fighting and reproducing. The principle chore of nervous systems is to get the body parts where they should be in order that the organism may survive…Improvements in sensorimotor control, confer an evolutionary advantage so long as it is geared to the organism’s way of life and enhances the organism’s chance of surivval. Truth, whatever that is, definitely takes the hindmost. -Patricia Churchland
In order to get 50%, he explained:
We know of no reason why the content of a belief should match what that belief (together, perhaps, with other structures) indicates. Content simply arises upon the appearance of neural structures of sufficient complexity; there is no reason why that content need be related to what the structures indicates, if anything. Indeed, the proposition constituting that content need not be so much as about that predator; it certainly need not be true.
What, then, is the likelihood that this proposition, this content, is true? Given just this much, shouldn’t we suppose that the proposition in question is as likely to be false as true? Here’s the picture: the NP [neurophysiological] properties of a belief are adaptive in that they cause adaptive behavior. Those NP properties also determine a content property. But as long as the NP properties are adaptive, it doesn’t matter, for survival and reproduction, what content is determined by those NP properties. The fact that these creatures have survived and evolved, that their cognitive equipment was good enough to enable their ancestors to survive and reproduce–that fact would tell us nothing at all about the truth of their beliefs or the reliability of their cognitive faculties. It would tell us nothing about the truth of the content of that belief: its contents might be true, but might with equal probability be false.
I tried to formalize method, and realized there are computational obstacles to doing it this way.
I set N=10,000. And I didn’t get anything over 7500 once for each 1000 numbers.
N=10000
n=1000
S=0
import random
for k in range(N):
t=0
for i in range(n): #Generate a True (1) or False (0) 1000 Times
x=random.randint(0,1)
t+=x
print(t)
if t>=750:
S+=1 #Add 1 to S for each True
print(S/N)
I realized that that make sense, since the answer is 10^-59, you’d get one on average of 10 with 59 zeroes behind it. Five zeroes isn’t nearly enough.
