[HN Gopher] Perplexing the Web, One Probability Puzzle at a Time
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       Perplexing the Web, One Probability Puzzle at a Time
        
       Author : isaacfrond
       Score  : 21 points
       Date   : 2024-09-02 12:17 UTC (3 days ago)
        
 (HTM) web link (www.quantamagazine.org)
 (TXT) w3m dump (www.quantamagazine.org)
        
       | gosub100 wrote:
       | my favorite problem, which I originally read here on hn:
       | 
       | > I have two children. One is a boy born on a Tuesday. What is
       | the probability I have two boys?
       | 
       | Apparently the answer is 13/27
       | 
       | https://math.stackexchange.com/questions/4400/boy-born-on-a-...
        
         | lupire wrote:
         | This is an example of how probability problems are ambiguously
         | or misleadingly stated, to make them seem harder than they are.
         | 
         | The top answer on SE explains why the wording you chose is
         | nonsensical.
        
       | Terretta wrote:
       | SPOILER
       | 
       | (Note that the article gives the answer, and discusses arriving
       | at the answer. So go read it first.)
       | 
       | Article opens with a puzzle. This is commenting on the article's
       | explanation .. avoiding some words in the comment just so this
       | spoiler doesn't spoil at a glance, you'd have to think about the
       | following before reading:
       | 
       | Curiously, this article doesn't touch on an instantaneous and no-
       | maths intuition about what can (or what cannot) come up as the
       | set after all draws including your first. There are a finite set
       | of possible sets in the container. One interesting set is ruled
       | out by the first draw: there is a set it _cannot_ be. With that
       | set ruled out, you 're at least (sets/(sets-1)) more likely the
       | next draw isn't from the missing and interesting set that is
       | intuitively known to you.
       | 
       | Further, if one did do this in the real world, it's intuitively
       | evident that the missing set and its inverse would be more likely
       | chosen for the container due to their interestingness. (The
       | article alludes to this when it points out the sets are not coin
       | flips, they are deliberately selected by a human.) That tips the
       | odds even more in your intuition's favor of how the probabilities
       | must balance.
       | 
       | So I'd argue this one is solvable by psychology as an alternative
       | to math.
        
         | lupire wrote:
         | Ypur first argument is interstellar but doesn't help solving,
         | because the set ruled out is exactly balanced (cancelled) by
         | the first red ball being removed from al the other sets.
         | 
         | The second paragraph just says that guesses can sometimes be
         | right, which of course is true, but doesn't tell us which
         | guesses are likely to be correct. Wha y you claim as
         | intuitively obvious at all, and I think most people would
         | _avoid_ those sets. And it 's not the same problem anyway,
         | because in the problem the sets are chosen randomly according
         | to a stated rule, not based on a personal bias.
        
           | Terretta wrote:
           | Exactly right, I'd missed: _" number of red balls by picking
           | a number between zero and 100 from a hat."_
           | 
           | Or, rather, when they talked about this being different from
           | a coin flip, I misremembered that the set was a human choice.
        
       | tzs wrote:
       | > [...] imagine that instead of starting with 100 balls, you
       | start with 101 balls in a row. Pick a ball at random. Then color
       | the balls to the left of it green and the ones to the right of it
       | red. Throw that ball away, leaving 100 balls.
       | 
       | > Then pick a second ball at random. That ball corresponds to the
       | first ball in the original problem. The problem tells you that
       | you picked a red ball, so it was to the right of the ball you
       | threw away. Now pick a third ball. This ball is either to the
       | left of the first ball, between the first ball and the second, or
       | to the right of the second. In two of the three possibilities,
       | the third ball is red. So the probability that the ball is red is
       | 2/3.
       | 
       | I think that explanation will confuse a lot of people. Let's say
       | the first ball picked is ball 94 (numbering from 1 to 101 left to
       | right). So 1-93 end up green and 95-101 are red. Let's say the
       | second ball is 98.
       | 
       | The third ball then must be from one of these three intervals:
       | green 1-93, red 95-97, red 99-101. Those intervals have lengths
       | 93, 3, and 3. OK, so now we've got three intervals which sounds
       | like the situation the explanation is talking about, and two of
       | those three give a red ball, so the probability the second ball
       | is red is 2/3.
       | 
       | But that is clearly wrong, since the probability of the ball
       | being picked from an interval is proportional to the length of
       | the interval. There is a 93/99 chance that ball 3 is green and
       | only a 6/99 chance it is red for these particular intervals.
       | 
       | It needs to be made clear that what you have to look at is all
       | possible ways to pick the three balls.
       | 
       | Maybe something like this. I'm going to call the position of the
       | ball that divides red and green D, which is an integer in
       | [1,101], P1 the position of the first colored ball picked, also
       | an integer in [1,101] not equal to D, and P2 the position of the
       | second colored ball, also an integer in [1,101] not equal to D or
       | P1.
       | 
       | Imagine a list of every possible legal (D, P1, P2) 3-tuple. Note
       | that each consists of 3 distinct integers in [1,101]. Partition
       | that list into several sublists, where two of the 3-tuples, (D,
       | P1, P2) and (D', P1', P2') are in the same sublist if and only if
       | they contain the same numbers. E.g., (5, 2, 12) and (2, 5, 12)
       | would be in the same sublist.
       | 
       | Each sublist will have 6 of the 3-tuples, one for each
       | permutation of the three distinct numbers shared by all the
       | 3-tuples in that sublist.
       | 
       | Each of those 6 permutations corresponds to a different ordering
       | of D, P1, and P2. For example (5, 2, 12) corresponds to the
       | ordering P1 < D < P2 and (2, 5, 12) corresponds to D < P1 < P2.
       | 
       | Here are the 6 possible orderings, and what the outcome is when
       | the balls are drawn:                 D < P1 < P2   P1 red, P2 red
       | D < P2 < P1   P1 red, P2 red       P1 < D < P2   P1 green, P2 red
       | P1 < P2 < D   P1 green, P2 green       P2 < D < P1   P1 red, P2
       | green       P2 < P1 < D   P1 green, P2 green
       | 
       | In the 3 cases where P1 is red, P2 is red in 2 of them and green
       | in 1. That gives a 2/3 chance of P2 being red if P1 was red.
       | 
       | This is true in all of the partitions of our list of all legal(D,
       | P1, P2), so is true in all cases.
        
         | smusamashah wrote:
         | What if there were just 4 balls instead of hundred. Second
         | probability of 2nd ball being of same color will be higher only
         | if the distribution of first was higher.
        
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