Probability- independent events

In summary, the conversation discusses the independence of events E and F_k, where E represents the first toss being a head and F_k represents a total of k heads in n tosses of a fair coin. The question is approached mathematically through Bayes theorem and the definition of conditional probability, resulting in the expression P(E|F_k)=P(E) and P(F_k|E)=P(F_k). This allows for the determination of the values of n and k for which E and F_k are independent events.
  • #1
C.E
102
0
1. A fair coin is tossed n times, let E be the event that the first toss is a head and Fk be the event that there are a total of k heads. For which values of n, k are E and fk independent events?

3. I don't see how these events can possibly be independent (surely the first influences the second). Could somebody please explain how to do this question?
 
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  • #2
You can approach this question mathematically instead of by conceptual reasoning. E and F_k are independent if P(E|F_k)=P(E) and P(F_k|E)=P(F_k).

Now you just have to find expressions for the above. Use Bayes theorem and the definition of conditional probability. After solving the above, you should be able to find n in terms of k.
 
  • #3


I understand your confusion about the concept of independent events. In probability, two events are considered independent if the occurrence of one event does not affect the probability of the other event. In the given scenario, the event E (first toss is a head) and Fk (total of k heads) can be independent for certain values of n and k, as long as the first toss does not have any influence on the number of heads in the total outcome.

To understand this better, let's take the example of flipping a fair coin 3 times (n = 3). In this case, E would be the event of getting a head on the first toss and Fk would represent the event of getting a total of k heads (k = 0, 1, 2, 3).
We can see that for k = 0, 1, 2, 3, the probability of Fk does not change regardless of the outcome of the first toss (E). Therefore, in this case, E and Fk are independent events.

However, if we consider a different scenario where n = 2 (coin is tossed only twice), then E and Fk are not independent events. This is because the outcome of the first toss (E) will directly affect the number of heads in the total outcome (Fk).

In summary, the independence of events E and Fk depends on the values of n and k. For certain values, they can be independent while for others, they are dependent. I hope this explanation helps you understand the concept better.
 

1. What is the definition of "probability-independent events"?

"Probability-independent events" refer to two or more events that do not affect each other in terms of their likelihood of occurring. In other words, the outcome of one event does not impact the probability of the other event happening.

2. How do you calculate the probability of independent events?

The probability of independent events can be calculated by multiplying the individual probabilities of each event. For example, if the probability of event A is 0.5 and the probability of event B is 0.3, the probability of both events occurring is 0.5 x 0.3 = 0.15.

3. What is the difference between independent and dependent events?

Independent events are events that do not affect each other's likelihood of occurring, while dependent events are events that do have an impact on each other's probability. For example, drawing two cards from a deck without replacement is a dependent event, as the probability of drawing the second card is affected by the outcome of the first draw.

4. Can events be both independent and dependent?

No, events are either independent or dependent, they cannot be both. However, it is possible for a series of events to have a mix of independent and dependent events within it.

5. How can knowledge of probability-independent events be useful in real life?

Understanding probability-independent events can be useful in decision-making and risk assessment. For example, if someone is flipping a coin multiple times, they can use the knowledge of independent events to predict the likelihood of a certain number of heads or tails appearing. This can also be applied to insurance and financial planning, as well as in fields such as medicine and engineering.

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