De Moivre-Laplace theorem help

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Homework Help Overview

The problem involves a voting scenario with 10,000,000 people, where 5,500,000 are expected to vote for party A and the remainder for party B. The question seeks to determine the probability that, from a random sample of 20,000 voters, more will vote for party B than for party A.

Discussion Character

  • Exploratory, Assumption checking, Mathematical reasoning

Approaches and Questions Raised

  • The original poster attempts to apply the de Moivre-Laplace theorem to calculate the probability, expressing concern about the validity of their approach given the resulting values of λ. Some participants suggest that the probability is expected to be very small, comparing it to a coin flip scenario to provide intuition.

Discussion Status

Participants are exploring the implications of the original poster's calculations and discussing the nature of the probability distribution involved. There is recognition of the counter-intuitive nature of the results, and some guidance has been offered regarding the expected size of the probability.

Contextual Notes

There is an emphasis on the assumptions regarding the distribution of voters and the implications of using the normal approximation in this context. The original poster's calculations involve specific values that raise questions about the appropriateness of the method used.

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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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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.
 
Last edited:
Thank you for the clarification. I guess it is just a bit counter-intuitive to me.
 
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##.
 
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
\phi(z) = \frac{1}{\sqrt{2 \pi}} e^{-z^2/2}
we have, for large ##z > 0##:
\Phi_{>}(z) \equiv \int_{z}^{\infty} \phi(t) \, dt \sim \frac{1}{z} \phi(z).
This follows from integration by parts: in ##\int \phi(t) \, dt,## take ##u = 1/t,
: dv = t \phi(t) \, dt = -d (\phi(t))##. Thus
\int_z^{\infty} \phi(t) \, dt = \phi(z)/z - \int_z^{\infty} \frac{1}{t^2} \phi(t) \, dt,
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}##.
 

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