De Moivre-Laplace theorem help

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In summary: So the probability of exceeding ##3\sigma## above the mean is already only about one in a thousand, and you are looking at the probability that it exceeds around ##14\sigma## above the mean. So I don't necessarily think you are doing anything wrong, but the answer is going to be tiny.
  • #1
2sin54
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Homework Statement


There are 10 000 000 people who are going to vote. 5 500 000 will vote for the party A, the rest will vote for the party B.
20 000 voters are randomly chosen. What is the probability that there will be more party B voters than there will be party A voters (sorry for poor translation).

2. The attempt at a solution

P(party A voter) = 0.55
P(party B voter) = 0.45

There have to be at least 10 001 party B voters for the condition to be satisfied. This number can range from 10 001 to 20 000. Then, according to de Moivre-Laplace theorem the probability which I need to find:

P = Θ(λ2) - Θ(λ1) ,

where λ1 = (10 001 - 20 000*0.45)/√(20 000*0.45*(1-0.45))

Unfortunately, λ1 ≈ 14.23. λ2 is even bigger. Both, when put under the Θ function asymptotically approximate to 1. Do I have to use a different approach here?
 
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  • #2
You should expect the probability to be extremely small. The probability distribution for the number of B voters is approximately normal with mean ##9000## and standard deviation ##\sigma = \sqrt{npq} \approx 70.4##. The probability of exceeding ##3\sigma## above the mean is already only about one in a thousand, and you are looking at the probability that it exceeds around ##14\sigma## above the mean. So I don't necessarily think you are doing anything wrong, but the answer is going to be tiny.
 
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  • #3
Thank you for the clarification. I guess it is just a bit counter-intuitive to me.
 
  • #4
Gytax said:
Thank you for the clarification. I guess it is just a bit counter-intuitive to me.
To get some intuition, consider flipping a coin 20000 times. Surely if the coin is fair (##p = q = 0.5##), it should be extremely unlikely that you would see, say, 11000 heads or more. Probably the only surprise is exactly how tiny the probability is, but you would expect it to be quite small. Your problem is similar, but with slightly different ##p## and ##q##.
 
  • #5
Gytax said:

Homework Statement


There are 10 000 000 people who are going to vote. 5 500 000 will vote for the party A, the rest will vote for the party B.
20 000 voters are randomly chosen. What is the probability that there will be more party B voters than there will be party A voters (sorry for poor translation).

2. The attempt at a solution

P(party A voter) = 0.55
P(party B voter) = 0.45

There have to be at least 10 001 party B voters for the condition to be satisfied. This number can range from 10 001 to 20 000. Then, according to de Moivre-Laplace theorem the probability which I need to find:

P = Θ(λ2) - Θ(λ1) ,

where λ1 = (10 001 - 20 000*0.45)/√(20 000*0.45*(1-0.45))

Unfortunately, λ1 ≈ 14.23. λ2 is even bigger. Both, when put under the Θ function asymptotically approximate to 1. Do I have to use a different approach here?

If you want an actual numerical approximation, use the fact that for the standard normal pdf
[tex] \phi(z) = \frac{1}{\sqrt{2 \pi}} e^{-z^2/2}[/tex]
we have, for large ##z > 0##:
[tex] \Phi_{>}(z) \equiv \int_{z}^{\infty} \phi(t) \, dt \sim \frac{1}{z} \phi(z).[/tex]
This follows from integration by parts: in ##\int \phi(t) \, dt,## take ##u = 1/t,
: dv = t \phi(t) \, dt = -d (\phi(t))##. Thus
[tex] \int_z^{\infty} \phi(t) \, dt = \phi(z)/z - \int_z^{\infty} \frac{1}{t^2} \phi(t) \, dt,[/tex]
and for large ##z > 0## we can drop the second term (its magnitude is ##< \phi(z)/z^2##). If you want better accuracy, get another term using integration by parts again, etc.

Anyway, for ##z = 14.23## we have ##\Phi_{>}(z) \approx 0.2999 \times 10^{-45}##.
 

What is the De Moivre-Laplace theorem?

The De Moivre-Laplace theorem, also known as the Central Limit Theorem, states that the distribution of a large number of independent and identically distributed random variables will approximate a normal distribution.

How is the De Moivre-Laplace theorem used?

The De Moivre-Laplace theorem is used in probability and statistics to calculate the likelihood of events occurring in a large sample size. It is also used to simplify complex probability distributions into a more manageable and easily interpretable normal distribution.

What is the formula for the De Moivre-Laplace theorem?

The formula for the De Moivre-Laplace theorem is: P(a < X < b) ≈ Φ((b-μ)/σ) - Φ((a-μ)/σ), where X is a random variable, μ is the mean, σ is the standard deviation, and Φ is the standard normal cumulative distribution function.

What are the assumptions of the De Moivre-Laplace theorem?

The De Moivre-Laplace theorem assumes that the random variables are independent and identically distributed, the sample size is large, and the variables have a finite mean and variance.

What are the limitations of the De Moivre-Laplace theorem?

The De Moivre-Laplace theorem may not accurately approximate a normal distribution if the sample size is small, the random variables are not independent and identically distributed, or if the mean and variance are not finite. Additionally, it only applies to continuous random variables, not discrete ones.

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