What Is the Probability That Player A Draws the Red Ball First?

In summary: P_k = \frac{B(B-1) \cdots (B-2k+1)}{N(N-1) \cdots (N-2k+1)} \frac{R}{N-2k}= \frac{({B \choose 2k})+({R \choose 2k-1})}{N(N-1) \cdots (N-2k+1)} \frac{R}{N-2k}
  • #1
CAF123
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Homework Statement


An urn contains 3 red and 7 black balls. A and B withdraw balls consecutively until a red ball is selected. Find the probability that A selects the red ball. (A draws the first ball.)

The Attempt at a Solution



A selects first => he will win with prob 3/10
B selects second => prob that he gets red is conditional on what colour A took.
P(B got red) = P(B got red| A got red)P(A got red) + P(B got red| A not got red)P(A not red) = (2/9)(3/10) + (3/9)(7/10) = 3/10. This seems nonintuitive - it seems the prob of B getting red is independent of what A took. Can someone explain this?

Where to go from here?
 
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  • #2
If B draws, then A has already drawn. How many balls are left after A draws?
 
  • #3
phinds said:
If B draws, then A has already drawn. How many balls are left after A draws?
B will have to choose from the remaining 9 balls
 
  • #4
CAF123 said:
B will have to choose from the remaining 9 balls

So why is it that you have a probability for B that is "... out of 10" ?
 
  • #5
CAF123 said:

Homework Statement


An urn contains 3 red and 7 black balls. A and B withdraw balls consecutively until a red ball is selected. Find the probability that A selects the red ball. (A draws the first ball.)

The Attempt at a Solution



A selects first => he will win with prob 3/10
B selects second => prob that he gets red is conditional on what colour A took.
P(B got red) = P(B got red| A got red)P(A got red) + P(B got red| A not got red)P(A not red) = (2/9)(3/10) + (3/9)(7/10) = 3/10. This seems nonintuitive - it seems the prob of B getting red is independent of what A took. Can someone explain this?

Where to go from here?

Explanation: you are neglecting the fact that if A draws a red ball the game stops and B does not get to draw a ball at all.

Your conclusion would be correct if B got to select a ball whether or not A got red; that is, even though B would be drawing from an urn now containing 9 balls, he would still have a 3/10 chance of getting red. To see this, number the balls from 1 to 10. There are 10! permutations altogether; A takes the first ball in line, then B takes the second, etc. The number of permutations in which the first ball is red is the same as in which the second ball is red, so A and B have the same probabilities of choosing red. In fact, this argument applies to any position, so if they continually choose balls without replacement (and don't look at the colors), then, when B chooses the very last ball he still has a 3/10 chance of choosing red! The argument is the same: the number of permutations in which a red ball is last is (3/10)*10! Students are often very surprised when they first see this.

RGV
 
  • #6
So this means the probability remains the same indep of what has been chosen before as long as we don't know what balls have been picked out before?

I solved this problem by noting down the combinations in which A would win:
R, BBR, BBBBR,BBBBBBR and I attained the right answer. Is there a more elegant approach? e.g say if the number of balls got very large, my method is inefficient.
 
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  • #7
CAF123 said:
Is there a more elegant approach?
Set up a recurrence relation. Define P(r, b) = prob that first player gets the first red, given r red and b black available. Consider the possible outcomes of the first player's draw, and thereby obtain an equation relating P(r, b) to P(r, b-1).
 
  • #8
CAF123 said:
So this means the probability remains the same indep of what has been chosen before as long as we don't know what balls have been picked out before?

I solved this problem by noting down the combinations in which A would win:
R, BBR, BBBBR,BBBBBBR and I attained the right answer. Is there a more elegant approach? e.g say if the number of balls got very large, my method is inefficient.

You can generalize and get a formula; then you can worry about the best way to evaluate that formula. If we have R red and B black balls, with N = R+B balls altogether, and if Mr. A starts, then if he wins he does so on an odd-numbered draw. Let Pk = probability that A wins on draw 2k+1 for k = 0,1,2,... . If A wins on draw 2k+1 the first 2k balls must be black and then the next one must be red, so
[tex] P_k = \frac{B(B-1) \cdots (B-2k+1)}{N(N-1) \cdots (N-2k+1)} \frac{R}{N-2k}
= \frac{{B \choose 2k}}{{N \choose 2k}}\frac{R}{N-2k} .[/tex]
Computationally, it might be better to go at it recursilvely:
[tex]
n:= B;\\
d:= B+R;\\
f:=n/d;\\
\text{for } j=1 \text{ to } 2k-1 \text{ do }\\
\mbox{ } n:=n-1;\\
\mbox{ } d:=d-1;\\
\mbox{ } f:=f*n/d;\\
\text{end for};\\
Pk:=f*R/(d-1); [/tex]
 

FAQ: What Is the Probability That Player A Draws the Red Ball First?

1. What is the Probability Urn Problem?

The Probability Urn Problem is a mathematical problem that involves randomly choosing objects from a set of objects contained in an urn. The objects can be of different types and may have different probabilities of being chosen. The goal is to determine the probability of selecting a certain combination of objects from the urn.

2. How do you solve the Probability Urn Problem?

To solve the Probability Urn Problem, you first need to determine the total number of objects in the urn and the number of each type of object. Then, you can use the formula P(E) = n(E)/n(S) to calculate the probability of selecting a specific combination of objects, where n(E) is the number of desired outcomes and n(S) is the total number of possible outcomes.

3. What is the difference between with replacement and without replacement in the Probability Urn Problem?

In the Probability Urn Problem, "with replacement" means that each time an object is selected from the urn, it is put back into the urn before the next selection is made. This means that the total number of objects in the urn stays the same for each selection. "Without replacement" means that once an object is selected, it is not put back into the urn, resulting in a different total number of objects for each subsequent selection.

4. Can the Probability Urn Problem be applied to real-world situations?

Yes, the Probability Urn Problem can be applied to real-world situations such as drawing cards from a deck, selecting objects from a bag, or choosing a sample from a population. It can also be used to model more complex scenarios, such as predicting the outcome of a series of events.

5. Are there any limitations to the Probability Urn Problem?

One limitation of the Probability Urn Problem is that it assumes all objects in the urn have an equal chance of being selected. In real-world situations, this may not always be the case. Additionally, the problem becomes more complex when dealing with large numbers of objects or when the probabilities of selecting each object are not known. In these cases, more advanced mathematical techniques may be needed to solve the problem.

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