Game Of Chance!

Three 6-sided fair dice are rolled together. Let P ( n ) P(n) denotes the probability of obtaining a total sum of n n . What is the relationship between P ( 9 ) P(9) and P ( 10 ) P(10) ?

P ( 9 ) < P ( 10 ) P(9) < P(10) P ( 9 ) = P ( 10 ) P(9) = P(10) P ( 9 ) > P ( 10 ) P(9) > P(10)

This section requires Javascript.
You are seeing this because something didn't load right. We suggest you, (a) try refreshing the page, (b) enabling javascript if it is disabled on your browser and, finally, (c) loading the non-javascript version of this page . We're sorry about the hassle.

5 solutions

Yee-Lynn Lee
Jul 28, 2016

9 9 can be obtained through the combinations: ( 6 , 1 , 2 ) (6,1,2) , ( 5 , 3 , 1 ) (5,3,1) , ( 5 , 2 , 2 ) (5,2,2) , ( 4 , 3 , 2 ) (4,3,2) , ( 4 , 1 , 4 ) (4,1,4) , ( 3 , 3 , 3 ) (3,3,3)

10 10 can be obtained through the combinations: ( 6 , 1 , 3 ) (6,1,3) , ( 6 , 2 , 2 ) (6,2,2) , ( 5 , 1 , 4 ) (5,1,4) , ( 5 , 3 , 2 ) (5,3,2) , ( 4 , 3 , 3 ) (4,3,3) , ( 4 , 2 , 4 ) (4,2,4)

However, just because there are 6 different combinations to obtain each number, doesn't mean they have the same probability.

If r 1 { r }_{ 1 } represents the number that is rolled on the first die, and r 2 { r }_{ 2 } and r 3 { r }_{ 3 } for the second and third dice, respectively, then, for example, the combination ( 6 , 1 , 2 ) (6,1,2) can be obtained by triplets ( r 1 , r 2 , r 3 ) ({ r }_{ 1 }, { r }_{ 2 }, { r }_{ 3 }) :

( 6 , 1 , 2 ) (6,1,2)

( 6 , 2 , 1 ) (6,2,1)

( 1 , 6 , 2 ) (1,6,2)

( 1 , 2 , 6 ) (1,2,6)

( 2 , 1 , 6 ) (2,1,6)

( 2 , 6 , 1 ) (2,6,1)

Any of the combinations above that have 3 distinct numbers have 6 permutations, while those with 1 repeated number have 3 permutations, and those that are comprised of 3 of the same number only have 1 permutation.

Both 9 and 10 have 3 combinations with 3 distinct numbers, but 9 has 2 combinations with 1 repeated number and 1 combination made of 3 of the same number, while 10 has 3 combinations 1 repeated number. Therefore, 10 has 27 permutations and a greater probability of being obtained than 9, with 25 permutations.

Thus, P ( 9 ) < P ( 10 ) P(9) < P(10) .

Nice explanation!

Hung Woei Neoh - 4 years, 10 months ago

Log in to reply

Thanks! :)

Yee-Lynn Lee - 4 years, 10 months ago
Ashish Sacheti
Jul 26, 2016

The expected value when you roll one dice is 3.5 so when you roll three die its 10.5 which is closer to 10 then 9.

Moderator note:

Is the presented reasoning logically correct?

Just because the expected value is closer, doesn't mean that it has a higher probability. For example, if there is a 99% chance of getting a 1, and a 1% chance of getting a 100, then the expected value is 1.99, which is very close to 2. However, you are more likely to get a 1, then you are to get to 2.

Calvin Lin Staff - 4 years, 10 months ago

Log in to reply

But there is symmetry since every number 1-6 is equally likely so then can't you use this expected value?

Ashish Sacheti - 4 years, 10 months ago

Log in to reply

Nope. What happens if the problem was for "probability of the product of the dice"? Then, does it mean that we are more likely to get 3. 5 2 3.5 ^ 2 as opposed to 15? Or are we more likely to get 13 as opposed to 15?

Calvin Lin Staff - 4 years, 10 months ago

Log in to reply

@Calvin Lin ahhh okay makes sense thank you

Ashish Sacheti - 4 years, 10 months ago

The sum will be normally distributed from 3-18 with 10.5 being in the center. The further away from the center of a normal distribution, the less likely it is. 10 is closer than 9 to 10.5, therefore it is more likely. If we were talking about non-fair dice or dice with random numbers (ex 1,2,3,4,5,100) I don't believe that this logic would hold.

Anthony Hill - 3 years, 6 months ago

Log in to reply

That's a great example. If we roll 3 dice (1, 2, 3, 4, 5, 100), we're much more likely to get a sum of 9 then we are to get a sum of 20 (or 15), even though the expected value is 115.

Note that the distribution of dice rolls isn't actually a normal distribution , though it can be approximated as one if we're throwing numerous dice. The property that you're going for is the shape of "one peak at the expected value that slopes away on both sides".

Calvin Lin Staff - 3 years, 6 months ago

This doesn't seem like a legitimate rebuttal to his argument. If the EV is 1.99, then since 1 is closer to 1.99 than 100 is to 1.99, choosing 1 is more likely than choosing 100. Just because 1.99 rounds off to 2 is irrelevant for the logical structure of the argument. Now, is his argument valid? I'm not entirely convinced, but surely this doesn't disprove it.

Jordan Katz - 1 year, 4 months ago

Log in to reply

Put another way, since E V = x f ( x ) EV = \sum x f(x) . It should make some sense that just because we know what E V EV is, doesn't give us much information about comparing f ( 9 ) , f ( 10 ) f(9), f(10) .

More specifically, if 0 < f ( 9 ) + f ( 10 ) < 1 0 < f(9) + f(10) < 1 , then the EV is determined by other x x values, and we can always find some distribution (with identical f ( 9 ) , f ( 10 ) f(9), f(10) values) to give any desired EV value. Hence, calculating EV doesn't tell us much about f ( 9 ) , f ( 10 ) f(9), f(10) .

Note: This argument can be modified to account for strict decreasing f ( x ) f(x) , and distributions with a single peak (increasing then decreasing).

Calvin Lin Staff - 1 year, 4 months ago
Hung Woei Neoh
Aug 3, 2016

Yee-Lynn has written a nice explanation for this problem. Here, I will use the lame method by listing out all possibilities.

The sample space of throwing 3 dice, S = { ( 1 , 1 , 1 ) , ( 1 , 1 , 2 ) , , ( 1 , 6 , 6 ) , ( 2 , 1 , 1 ) , , ( 2 , 6 , 6 ) , , ( 6 , 6 , 6 ) } S =\{(1,1,1),(1,1,2),\ldots,(1,6,6),(2,1,1),\ldots,(2,6,6),\ldots,(6,6,6)\}

n ( S ) = 6 3 = 216 n(S) = 6^3 = 216

Define event A A as getting a 9 9 and event B B as getting a 10 10 .

A = { ( 1 , 2 , 6 ) , ( 1 , 3 , 5 ) , ( 1 , 4 , 4 ) , ( 1 , 5 , 3 ) , ( 1 , 6 , 2 ) , ( 2 , 1 , 6 ) , ( 2 , 2 , 5 ) , ( 2 , 3 , 4 ) , ( 2 , 4 , 3 ) , ( 2 , 5 , 2 ) , ( 2 , 6 , 1 ) , ( 3 , 1 , 5 ) , ( 3 , 2 , 4 ) , ( 3 , 3 , 3 ) , ( 3 , 4 , 2 ) , ( 3 , 5 , 1 ) , ( 4 , 1 , 4 ) , ( 4 , 2 , 3 ) , ( 4 , 3 , 2 ) , ( 4 , 4 , 1 ) , ( 5 , 1 , 3 ) , ( 5 , 2 , 2 ) , ( 5 , 3 , 1 ) , ( 6 , 1 , 2 ) , ( 6 , 2 , 1 ) } A = \{(1,2,6),(1,3,5),(1,4,4),(1,5,3),(1,6,2),\\ (2,1,6),(2,2,5),(2,3,4),(2,4,3),(2,5,2),(2,6,1),\\ (3,1,5),(3,2,4),(3,3,3),(3,4,2),(3,5,1),\\ (4,1,4),(4,2,3),(4,3,2),(4,4,1),\\ (5,1,3),(5,2,2),(5,3,1),\\ (6,1,2),(6,2,1)\}

n ( A ) = 25 n(A)=25

Therefore, P ( 9 ) = P ( A ) = n ( A ) n ( S ) = 25 216 P(9)=P(A)=\dfrac{n(A)}{n(S)}=\dfrac{25}{216}

B = { ( 1 , 3 , 6 ) , ( 1 , 4 , 5 ) , ( 1 , 5 , 4 ) , ( 1 , 6 , 3 ) , ( 2 , 2 , 6 ) , ( 2 , 3 , 5 ) , ( 2 , 4 , 4 ) , ( 2 , 5 , 3 ) , ( 2 , 6 , 2 ) , ( 3 , 1 , 6 ) , ( 3 , 2 , 5 ) , ( 3 , 3 , 4 ) , ( 3 , 4 , 3 ) , ( 3 , 5 , 2 ) , ( 3 , 6 , 1 ) , ( 4 , 1 , 5 ) , ( 4 , 2 , 4 ) , ( 4 , 3 , 3 ) , ( 4 , 4 , 2 ) , ( 4 , 5 , 1 ) , ( 5 , 1 , 4 ) , ( 5 , 2 , 3 ) , ( 5 , 3 , 2 ) , ( 5 , 4 , 1 ) , ( 6 , 1 , 3 ) , ( 6 , 2 , 2 ) , ( 6 , 3 , 1 ) } B=\{(1,3,6),(1,4,5),(1,5,4),(1,6,3),\\ (2,2,6),(2,3,5),(2,4,4),(2,5,3),(2,6,2),\\ (3,1,6),(3,2,5),(3,3,4),(3,4,3),(3,5,2),(3,6,1),\\ (4,1,5),(4,2,4),(4,3,3),(4,4,2),(4,5,1),\\ (5,1,4),(5,2,3),(5,3,2),(5,4,1),\\ (6,1,3),(6,2,2),(6,3,1)\}

n ( B ) = 27 n(B)=27

Therefore, P ( 10 ) = P ( B ) = n ( B ) n ( S ) = 27 216 P(10)=P(B)=\dfrac{n(B)}{n(S)}=\dfrac{27}{216}

From here, we know that P ( 9 ) < P ( 10 ) \boxed{P(9)<P(10)}

Probabilities are normally distributed for this case. The min case is {1,1,1} = 3 The max case is {6,6,6}=18 This would happen only once.

The second min case is 4, that can happen by 3 ways. {1,2,1},{2,1,1} and {1,1,2}. Similarly second max case is 17, also can happen 3 ways {6,6,5},{6,5,6},{5,6,6}.

Thus we would get the maximum frequencies at the mid pair. We have got (18-3)+1= 16 different Sums and that means 16/2= 8 pairs.

The middle value between them is 3+8=11 and 3+8-1=10 is the medians. So, the sums 10 and 11 would happen for most of the times.

Therefore, P(10)> P(9)

সাজিদ হাসান - 4 years, 1 month ago
Dmitry Nikolaev
Jun 21, 2017

Relative probabilities of different outcomes are encoded by coefficients of the the polynomial ( x + x 2 + . . . + x 6 ) 6 (x + x^{2} + ... + x^{6})^{6} (cf. the Generating functions wiki). One can compute them directly or notice that their values increase monotonically up to the median power on the interval [6, 36] and then decrease. 10 is closer to 21 than 9, so necessarily its coefficient will be greater.

Very nice. You made a slight mistake though, there are just three dice not six. Otherwise the insight is really nice. Actually one can take from each generating function the median exponents which are 3 and 4, adding them three times respectively (there are three dice), one gets 9 and 12. These two sandwich the two most frequent exponents, 10 and 11.

Norbert Madarász - 3 years, 5 months ago

Nice solution

Jassy Kal - 7 months ago
Douglas Vieira
Feb 23, 2020

The sum of the dice follows a concave symmetrical distribution over the values 3, 4, ..., 18. Since 10 is the midpoint of such range, P(10) is the highest, thus P(10) > P(9).

0 pending reports

×

Problem Loading...

Note Loading...

Set Loading...