Understanding Combinations with Replacement in Probability

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In summary, the number of possible outcomes for distributing 5 distinct awards among 30 students, where any student can receive more than 1 award, is 30^5. This is because each time a medal is awarded, there are 30 possible students that can receive it, resulting in 30x30x30x30x30=30^5 possible outcomes. This can also be visualized as a branching tree.
  • #1
hotvette
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1. Homework Statement

If a total of 5 distinct awards are distributed among 30 students where any student can receive more than 1 award, how many possible outcomes are there?

2. Homework Equations
[tex] \text{outcomes} = r^n [/tex]
where r is the number of choices and n is the number of draws.

3. The Attempt at a Solution

I know the answer is [itex]30^5[/itex] but I don't see why 30 is the number of choices and 5 is the number of draws. I know in the case of how many numbers can be formed using 8 binary digits is [itex]2^8[/itex]. In this case it kind of makes sense that there are only two choices (0 or 1) and I perform the operation 8 times, but with the award and student problem is confusing to me. I just don't see how 30 is the number of choices. I can just as easily say 5 is the number of choices; a student can have up to 5 awards. Only the other hand, only 1 student could get 5 awards, so it really isn't the same as the binary number problem.

What is the thought process to properly sort this out? I've also seen explanations in terms of bins and balls but it's tough to figure out which is the bin and which is the ball. There is something conceptually I'm not getting. Can someone explain?
 
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  • #2
Each time a medal is awarded, there are 30 possible students that receive it. Based on that possible outcome, there are then 30 more possible more outcomes relative to the next medal, etc.

30 possible students get the 1st medal of 5 medals = 30 possible outcomes
30 possible students get the 2nd medal of 5 medals = 30 possible outcomes
30 possible students get the 3rd medal of 5 medals = 30 possible outcomes
30 possible students get the 4th medal of 5 medals = 30 possible outcomes
30 possible students get the 5th medal of 5 medals = 30 possible outcomes

Therefore, it's 30 x 30 x 30 x 30 x 30 possible outcomes.

Sometimes it helps to view it as a branching tree. Assume 3 students (A, B, C) and 3 medals (1, 2, 3).

(A1, B1, C1) is the first set of possibilities
Then, relative to each of those possibilities, (A2, B2 or C2) is the next branch
Then, relative to each of those possibilities, (A3, B3, C3) for the next branch

A1, A2, A3
A1, A2, B3
A1, A2, C3

A1, B2, A3
A1, B2, B3
A1, B2, C3

A1, C2, A3
A1, C2, B3
A1, C2, C3

B1, A2, A3
B1, A2, B3
B1, A2, C3

B1, B2, A3
B1, B2, B3
B1, B2, C3

B1, C2, A3
B1, C2, B3
B1, C2, C3

C1, A2, A3
C1, A2, B3
C1, A2, C3

C1, B2, A3
C1, B2, B3
C1, B2, C3

C1, C2, A3
C1, C2, B3
C1, C2, C3
 
  • #3
Geesh, it seems too easy when explained clearly. Thanks!.
 
  • #4
hotvette said:
Geesh, it seems too easy when explained clearly. Thanks!.

You are welcome. I'm glad it makes sense.
 

1. What is "counting with replacement" in scientific terms?

"Counting with replacement" refers to a probability experiment where an item is selected from a group and then returned to the group before the next selection. This means that each time an item is selected, it still has the same probability of being chosen again, regardless of previous selections.

2. How is "counting with replacement" different from "counting without replacement"?

"Counting without replacement" refers to a probability experiment where an item is selected from a group and then not returned to the group before the next selection. This means that each time an item is selected, the probability of selecting it again decreases because there are fewer items in the group.

3. What is the formula for calculating the probability in "counting with replacement"?

The formula for calculating the probability in "counting with replacement" is P(event) = number of desired outcomes / total number of possible outcomes. This is the same formula used in basic probability, but in this case, the number of possible outcomes remains the same for each selection since items are replaced after each selection.

4. Can "counting with replacement" be applied in real-life situations?

Yes, "counting with replacement" can be applied in real-life situations. For example, flipping a coin multiple times and replacing it back into the pile each time is a form of "counting with replacement". It is also commonly used in card games and genetics experiments.

5. What are some limitations of "counting with replacement" in scientific research?

One limitation of "counting with replacement" is that it assumes that the probability of selecting an item remains constant for each selection, which may not always be the case in real-life situations. Additionally, it may not accurately reflect the true probabilities in situations where there are a limited number of items in the group.

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