Understanding Independence in Probability: An Example

In summary, the conversation discusses the concept of conditional probability and provides an example of P(AB) < P(A)P(B). The example involves a coin toss where A represents the event of getting heads and B represents the event of getting tails. The confusion arises from the incorrect calculation of P(AB) as \frac{1}{4}, when it should be 0. This is because the events A and B cannot occur simultaneously, making their joint probability 0.
  • #1
mateomy
307
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The question in my book is posed as:

Find an example in which [itex]P(AB) < P(A)P(B)[/itex].
The answer is given as a coin toss letting A be the event of obtaining heads and B being the even of obtaining tails. I'm confused as to how that's working.

My understanding is no doubt 'off', but I thought it was
[tex]
P(\frac{1}{2} * \frac{1}{2}) = \frac{1}{2} * \frac{1}{2}
[/tex]
because the probabilities for both are [itex]\frac{1}{2}[/itex]. So they are equivalent at [itex]\frac{1}{4}[/itex]. Clearly I'm incorrect, can someone point out why?

Thanks.
 
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  • #2
mateomy said:
The question in my book is posed as:

Find an example in which [itex]P(AB) < P(A)P(B)[/itex].
The answer is given as a coin toss letting A be the event of obtaining heads and B being the even of obtaining tails. I'm confused as to how that's working.

My understanding is no doubt 'off', but I thought it was
[tex]
P(\frac{1}{2} * \frac{1}{2}) = \frac{1}{2} * \frac{1}{2}
[/tex]
because the probabilities for both are [itex]\frac{1}{2}[/itex]. So they are equivalent at [itex]\frac{1}{4}[/itex]. Clearly I'm incorrect, can someone point out why?

Thanks.

AB is shorthand for the cases where A is true AND B is true. Wouldn't it be true that the probability of getting heads AND tails is 0?
 
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Likes 1 person
  • #3
That clarifies everything. Thank you.
 

Related to Understanding Independence in Probability: An Example

1. What is independence in probability?

Independence in probability refers to the relationship between two events, where the occurrence of one event does not affect the likelihood of the other event occurring. In other words, the outcome of one event has no influence on the outcome of the other event.

2. How do you determine if two events are independent?

To determine if two events are independent, you can use the formula P(A∩B) = P(A) * P(B), where P(A) represents the probability of event A occurring, P(B) represents the probability of event B occurring, and P(A∩B) represents the probability of both events A and B occurring together. If the equation holds true, the events are independent.

3. Can two independent events occur simultaneously?

Yes, two independent events can occur simultaneously. This means that the occurrence of one event does not affect the likelihood of the other event occurring, so they can both happen at the same time.

4. How does independence affect the calculation of probabilities?

If two events are independent, the calculation of probabilities is simplified. Instead of using the formula P(A∩B) = P(A) * P(B), you can simply multiply the individual probabilities of each event. This is because the outcome of one event has no influence on the outcome of the other event, so the probabilities can be multiplied together.

5. What are some real-life examples of independent events?

Some real-life examples of independent events include flipping a coin and rolling a die. The outcome of flipping a coin has no influence on the outcome of rolling a die, so these events are independent. Another example is spinning a roulette wheel and drawing a card from a deck. The result of one event does not affect the likelihood of the other event occurring.

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