Calculating Conditional Probability: Two Heads Result Given At Least One Head

In summary: No, I don't think so. There is a pre-condition that at least one of the coins has a head, so you CANNOT have tail #1 and tail #2. It's ruled out
  • #1
phospho
251
0
Two coins are tossed. What is the conditional probability that two heads result given that there is a t least one head?

let S be the sample space

S = {{HH},{HT},{TH,{TT}}

P(HH|HT,TH,HH) = P(HH n HT,TH,HH)/P(HT,TH,HH) = (1/4 * 3/4) / (3/4) = 1/4

is this correct? I'm finding it hard to determine what the question is actually asking
 
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  • #2
phospho said:
Two coins are tossed. What is the conditional probability that two heads result given that there is a t least one head?

let S be the sample space

S = {{HH},{HT},{TH,{TT}}

P(HH|HT,TH,HH) = P(HH n HT,TH,HH)/P(HT,TH,HH) = (1/4 * 3/4) / (3/4) = 1/4

is this correct? I'm finding it hard to determine what the question is actually asking

Think about what the question says: what is the conditional probability of two heads if you are told you get at least one head? The information you are told eliminates one or more of the original sample points; can you say which sample point (or points) is (or are) eliminated? For the points that remain, what are the new, adjusted, probabilities?
 
  • #3
"conditional" means what it says --- a condition has been imposed. So here are two different questions:

What are the odds that two coin tosses in a row will result in two heads?

What are the odds that two coin tosses in a row will result in two heads if you add the condition that it is known that one of the tosses results in a head?
 
  • #4
{{H,H}, {H, T}, {T, H}, {T,T}} is the sample space for "flip two coins and observe which side is up". The condition, "at least one head" reduces that to {{H, H}, {H, T}, {T, H}}. What fraction of that is "two heads"?
 
  • #5
phospho said:
P(HH n HT,TH,HH)/P(HT,TH,HH) = (1/4 * 3/4) / (3/4)
You appear to have evaluated P(HH n HT,TH,HH) as P(HH)*P(HT,TH,HH). The two are not independent, so you cannot multiply their probabilities. Indeed, one is a subset of the other, so P(HH n HT,TH,HH) = P(HH).
 
  • #6
HallsofIvy said:
{{H,H}, {H, T}, {T, H}, {T,T}} is the sample space for "flip two coins and observe which side is up". The condition, "at least one head" reduces that to {{H, H}, {H, T}, {T, H}}. What fraction of that is "two heads"?

so it's 1/3?
 
  • #7
phospho said:
so it's 1/3?

That's not what I get.
 
  • #8
phinds said:
That's not what I get.

here is my working:

P(HH|HT,TH,HH) = P(HH)/P(HT,TH,HH) = (1/4)/(3/4) = 1/3
 
  • #9
Here's my working for coin 1 / coin 2

H/H
H/T
T/H
H/H

the two HH's are NOT the same. They are the same only if you are looking for COMBINATIONS, not probabilities.
 
  • #10
phinds said:
Here's my working for coin 1 / coin 2

H/H
H/T
T/H
H/H

the two HH's are NOT the same. They are the same only if you are looking for COMBINATIONS, not probabilities.

Every probability book I have ever seen would say there is just one HH if the coins are undistinguished (i.e., maybe distinguishable, but we don't bother to do it). You *would* be correct if you distinguished between the two coins, say one is red and the other is blue. Then the sample space would be {(RH,BH),(BH,RH),(RH,BT),(BH,RT),(RT,BH),(BT,RH),(BT,RT),(RT,BT)}, with (presumably) each sample point (i,j) having probability 1/8. One could then work the problem for this representation, but the final answer would be the same as in the simpler, undistinguished case.
 
  • #11
Ray Vickson said:
Every probability book I have ever seen would say there is just one HH if the coins are undistinguished (i.e., maybe distinguishable, but we don't bother to do it). You *would* be correct if you distinguished between the two coins, say one is red and the other is blue.

But you CAN, and I believe MUST, distinguish between the coins. One is tossed first and the other is tossed second, and this gives 4 outcomes, as I stated.
 
  • #12
phinds said:
But you CAN, and I believe MUST, distinguish between the coins. One is tossed first and the other is tossed second, and this gives 4 outcomes, as I stated.

But that is related to what I said in my post. You have 8 (NOT 4) equally-likely outcomes. I listed them all. You cannot just distinguish between head #1 and head #2 and ignore all the others: you also need to distinguish between, eg., head#1 and tail#2, head #2 and tail #1, tail #1 and tail #2, etc., etc.
 
  • #13
Ray Vickson said:
But that is related to what I said in my post. You have 8 (NOT 4) equally-likely outcomes. I listed them all. You cannot just distinguish between head #1 and head #2 and ignore all the others: you also need to distinguish between, eg., head#1 and tail#2, head #2 and tail #1, tail #1 and tail #2, etc., etc.

No, I don't think so. There is a pre-condition that at least one of the coins has a head, so you CANNOT have tail #1 and tail #2. It's ruled out.
 
  • #14
phinds said:
No, I don't think so. There is a pre-condition that at least one of the coins has a head, so you CANNOT have tail #1 and tail #2. It's ruled out.

Yes, I know that. I start with the complete sample space, having 8 equiprobable points. After conditioning, you would be left with 6 (NOT 4) equiprobable points (eliminating (T#1,T#2) and (T#2,T#1)). Among these 6 points, 2 of them correspond to two heads, so we get a conditional probability of 2/6 = 1/3, as before.
 
  • #15
OK, I see the error of my ways ... it's 1/3

Thanks
 

1. How is conditional probability calculated?

Conditional probability is calculated by dividing the probability of the joint occurrence of two events by the probability of the occurrence of one of those events. In this case, it would be the probability of getting two heads on a coin flip, divided by the probability of getting at least one head on a coin flip.

2. What is the formula for calculating conditional probability?

The formula for calculating conditional probability is: P(A|B) = P(A and B) / P(B), where A and B are events and P(A|B) is the conditional probability of event A given event B.

3. How do you interpret conditional probability?

Conditional probability is the likelihood of an event occurring given that another event has already occurred. It is used to understand the relationship between two events and how the occurrence of one event affects the probability of the other event.

4. Can conditional probability be greater than 1?

No, conditional probability cannot be greater than 1. This is because the probability of an event cannot exceed the probability of the sample space, which is always equal to 1.

5. How is conditional probability different from regular probability?

Regular probability is the likelihood of an event occurring without any prior knowledge or conditions, whereas conditional probability takes into account the occurrence of another event. In other words, regular probability is the probability of A, while conditional probability is the probability of A given B.

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