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Impact of Information: Conditional Probability

Revisiting Conditional Probability

Conditional probability deals with the likelihood of an event occurring given that another event has already occurred or is known to be true. This "given information" effectively reduces our sample space to only those outcomes consistent with the known information.

The formula is P(A|B) = P(A ∩ B) / P(B), where P(A|B) is the probability of A given B, P(A ∩ B) is the probability of both A and B occurring, and P(B) is the probability of B occurring (and P(B) > 0).

Careful Definition of Sample Space

Many classic probability puzzles hinge on correctly identifying the initial sample space and then how it changes once certain information is provided. It's crucial not to make intuitive leaps without rigorously considering all possibilities within the (potentially new) relevant sample space. The "Two Children Problem" is a prime example of this, often leading to counter-intuitive results if the sample space isn't handled correctly.

Two Children, At Least One Boy

MODERATE

A family has two children. You are given the information that at least one of the children is a boy. What is the probability that both children are boys?

Variation to Consider: How does the probability change if the information given was: "The older child is a boy. What is the probability both are boys?" Why is this different?

 

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