Legend Of ~Incredim's question at Yahoo Answers regarding binomial probability

In summary, we can estimate the probability of getting exactly 275 heads in 400 tosses of a loaded coin with a 0.65 chance of landing heads to be approximately 0.0122157679732721. This is calculated using the special multiplication and addition rules, as well as the binomial probability formula.
  • #1
MarkFL
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Here is the question:

Legend Of ~Incredim said:
A coin is loaded so that the chance of getting heads in a single toss is 0.65. If the coin is tossed 400 times, estimate the probability?

of getting EXACTLY 275 heads

I have posted a link there to this thread so the OP can view my work.
 
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  • #2
Hello Legend Of ~Incredim,

First, we want to consider how many different ways there are to have 275 heads in a total of 400 tosses. This is equivalent to asking how may ways there are to choose 275 from 400, and is given by:

\(\displaystyle {400 \choose 275}\)

Of these different choices, 275 are heads and have a probability of 0.65 and 125 are tails with a probability of 0.35. So, for one particular choice, for example the first 275 tosses are heads and the remainder are tails, we have by the special multiplication rule that the probability for that particular choice is:

\(\displaystyle (0.65)^{275}(0.35)^{125}\)

And then by the special addition rule, we find the total probability is:

\(\displaystyle P(\text{exactly 275 heads})={400 \choose 275}(0.65)^{275}(0.35)^{125}\approx0.0122157679732721\)

This result is what would be suggested by the binomial probability formula.
 

FAQ: Legend Of ~Incredim's question at Yahoo Answers regarding binomial probability

What is the Legend Of ~Incredim's question at Yahoo Answers regarding binomial probability?

The Legend Of ~Incredim's question at Yahoo Answers is a hypothetical scenario where a person named Incredim claims to have the ability to predict the outcome of a coin toss with 80% accuracy. The question asks how many correct predictions Incredim would need to make in order to prove their claim statistically significant.

What is binomial probability?

Binomial probability is a mathematical concept that calculates the likelihood of a specific number of successes occurring in a fixed number of trials, where each trial has only two possible outcomes (success or failure). It is used to analyze and predict the occurrence of events with only two possible outcomes, such as coin flips or yes/no questions.

How is binomial probability calculated?

Binomial probability is calculated using the formula P(x) = nCx * p^x * q^(n-x), where n is the number of trials, x is the number of successes, p is the probability of success, and q is the probability of failure. nCx is the binomial coefficient, which represents the number of ways x successes can occur in n trials.

How does the accuracy of predictions affect binomial probability?

The accuracy of predictions does not directly affect binomial probability. The probability of success (p) remains constant in each trial, regardless of the accuracy of predictions. However, if the accuracy of predictions is high, the number of successes (x) in a fixed number of trials (n) will likely be higher, resulting in a higher overall probability of success.

What are the limitations of using binomial probability?

Binomial probability is only applicable to events with two possible outcomes and assumes that each trial is independent and has the same probability of success. It also does not take into account external factors that may affect the outcome of the event. Additionally, it can only be used for a fixed number of trials, which may not accurately represent real-world situations where the number of trials is not predetermined.

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