What is the probability of drawing a spade from a deck of 51 cards?

In summary: P(A) = P(A | S)P(S) + P(A|S^c)P(S^c)P(S|lost card was a spade) = P(S|lost card was not a spade) = 1/4P(S|lost card was not a spade) = P(S|lost card was not a spade) + P(S|event card was drawn from the deck given drawn card is not a spade) = 1/2
  • #1
hholzer
37
0
Suppose you had a normal deck of 52 playing
cards and lost a card. You then decide to draw
a card from the remaining 51 cards.

What is the probability the drawn card is a spade?

Would this be appropriately captured by the following
events:
A : event card was drawn from the deck
S : event card drawn is a spade
S^c : event card drawn is not a spade

then

P(A) = P(A | S)P(S) + P(A|S^c)P(S^c)

But this is annoying me because
if we called S "event card drawn is a spade"
and A "event card was drawn from deck"
then P(A | S) doesn't seem to make much
sense to me. That is, "event card drawn
from the deck given drawn card is a spade"
is pretty much incoherent.

What am I missing or how can I resolve this issue?
 
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  • #2
hi hholzer! :smile:
hholzer said:
Suppose you had a normal deck of 52 playing
cards and lost a card. You then decide to draw
a card from the remaining 51 cards.

What is the probability the drawn card is a spade?

Would this be appropriately captured by the following
events:
A : event card was drawn from the deck …

(btw, that's not the way we use the word "event" :wink:)

I don't understand what your A is supposed to be :confused:

You want P(S) …

split it up into P(S|lost card was a spade) and P(S|lost card was not a spade) :smile:

(are you sure you've copied the question correctly? it seems obvious the answer is 0.25 :confused:)
 
  • #3
Ah, that's what I was trying to determine. So we break it up into
(Lost card was spade) and (Lose card not spade).

The answer is indeed 1/4 but I was more concerned
with how we partition the sample space.

And on the word "event", "event" is a subset of your sample space,
as you of course know. The three events would be:

S = {card randomly drawn from deck of 51 cards is a spade }
A = {lost card is a spade }
A^c = { lost card is not a spade }
 

What is the Law of Total Probability?

The Law of Total Probability is a fundamental principle in probability theory that states that the probability of an event occurring is equal to the sum of the probabilities of all possible outcomes of that event.

How is the Law of Total Probability used?

The Law of Total Probability is used to calculate the probability of an event when there are multiple possible outcomes, each with their own probability. It allows us to take into account all possible scenarios when determining the likelihood of an event.

What is the formula for the Law of Total Probability?

The formula for the Law of Total Probability is P(A) = P(A|B1)P(B1) + P(A|B2)P(B2) + ... + P(A|Bn)P(Bn), where P(A) is the probability of event A, P(B1) through P(Bn) are the probabilities of all possible outcomes B1 through Bn, and P(A|B1) through P(A|Bn) are the conditional probabilities of A given each outcome B1 through Bn.

Can the Law of Total Probability be applied to any type of probability problem?

Yes, the Law of Total Probability is a general principle that can be used to solve any type of probability problem, as long as all possible outcomes are known and the probabilities of each outcome are known or can be calculated.

What is the difference between the Law of Total Probability and Bayes' Theorem?

While both the Law of Total Probability and Bayes' Theorem involve calculating probabilities using conditional probabilities, the main difference is that the Law of Total Probability is used to calculate the probability of an event before any information is known, while Bayes' Theorem is used to update the probability of an event after new information is received.

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