# What is the meaning of conditional probability?

Conditional probability is the probability of a given event assuming that you know the outcome of another event.

Take for example two dependent variables. Define A as being "A random American president's first name is George" and B to be "A random American president's last name is Bush."

Overall, there have been 44 presidents, of which 3 have been named George. 2 of the 44 have been named Bush.

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Conditional probability is a measure of the likelihood of an event occurring given that another event has already occurred. It represents the probability of event A happening given that event B has already occurred, and is denoted as ( P(A|B) ), read as "the probability of A given B."

Mathematically, conditional probability is calculated using the formula:

[ P(A|B) = \frac{P(A \cap B)}{P(B)} ]

Where:

- ( P(A|B) ) is the conditional probability of event A given event B.
- ( P(A \cap B) ) is the probability of both events A and B occurring simultaneously.
- ( P(B) ) is the probability of event B occurring.

In simpler terms, conditional probability helps us understand how the probability of one event changes based on the occurrence of another event. It is fundamental in various fields such as statistics, probability theory, and decision-making processes.

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When evaluating a one-sided limit, you need to be careful when a quantity is approaching zero since its sign is different depending on which way it is approaching zero from. Let us look at some examples.

When evaluating a one-sided limit, you need to be careful when a quantity is approaching zero since its sign is different depending on which way it is approaching zero from. Let us look at some examples.

When evaluating a one-sided limit, you need to be careful when a quantity is approaching zero since its sign is different depending on which way it is approaching zero from. Let us look at some examples.

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