Deducing Sample Size from Sample Proportion CI

In summary, to estimate a sample proportion to within 0.05 with 95% confidence, the sample size should be at least 384, regardless of the actual values of p and q. This may result in performing more experiments than necessary, but it ensures that the sample size is not too small.
  • #1
pluviosilla
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Homework Statement



Suppose we want to estimate a sample proportion to within 0.05 with 95% confidence, how large will the sample have to be?

There is no other information about the distributions of the population or the sample.

How do I approach this problem?

Homework Equations



See attempted solution below.

for a proportion problem, sample stddev, sigma x bar = sqrt(pq/n)

The Attempt at a Solution



No point in trying to solve for n in the equation 1.96 * sqrt(pq / n) = 0.05, because I don't know what pq is.

(This is NOT homework. I'm a 62 year-old brushing up on statistics for professional reasons, but one of your "mentors" with a bike-riding avatar is worried that I might be cheating on a high school exam, so he sent me here. Maybe I'm in my 2nd childhood, which is why I provoke these suspicions. I got some *great* responses to more difficult statistics problems on this forum back in 2012. I do hope your new policies have not rendered this resource useless to me. I will go bug people on the actuarial forums if you force me to, though I'd rather not because I think this is a more appropriate venue - or used to be, anyway.)
 
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  • #2
pluviosilla said:

Homework Statement



Suppose we want to estimate a sample proportion to within 0.05 with 95% confidence, how large will the sample have to be?

There is no other information about the distributions of the population or the sample.

How do I approach this problem?

Homework Equations



See attempted solution below.

for a proportion problem, sample stddev, sigma x bar = sqrt(pq/n)

The Attempt at a Solution



No point in trying to solve for n in the equation 1.96 * sqrt(pq / n) = 0.05, because I don't know what pq is.

(This is NOT homework. I'm a 62 year-old brushing up on statistics for professional reasons, but one of your "mentors" with a bike-riding avatar is worried that I might be cheating on a high school exam, so he sent me here. Maybe I'm in my 2nd childhood, which is why I provoke these suspicions. I got some *great* responses to more difficult statistics problems on this forum back in 2012. I do hope your new policies have not rendered this resource useless to me. I will go bug people on the actuarial forums if you force me to, though I'd rather not because I think this is a more appropriate venue - or used to be, anyway.)

You may not know what ##p## and ##q = 1-p## are, but ##pq = p(1-p)## has a maximum over ##0 \leq p \leq 1## at the point ##p = 1/2##, where it equals ##1/4##. That is, ##p q \leq 1/4## for all ##p \in [0,1]##. If you replace ##pq## in your formula above by ##1/4## and then solve for ##n##, that ##n## will be larger (or, at least, not smaller than) the actual ##n## that would go along with ##p,q## if you knew what they were. In other words, you can't go wrong if you solve
[tex] 1.96 \sqrt{0.25/n} = 0.05.[/tex]
You may be using a larger ##n## than you really need, so you may be performing more experiments than you really require. The point is, though, that you will not be performing fewer experiments than you really need.
 
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  • #3
Ray Vickson said:
You may not know what ##p## and ##q = 1-p## are, but ##pq = p(1-p)## has a maximum over ##0 \leq p \leq 1## at the point ##p = 1/2##, where it equals ##1/4##. That is, ##p q \leq 1/4## for all ##p \in [0,1]##. If you replace ##pq## in your formula above by ##1/4## and then solve for ##n##, that ##n## will be larger (or, at least, not smaller than) the actual ##n## that would go along with ##p,q## if you knew what they were. In other words, you can't go wrong if you solve
[tex] 1.96 \sqrt{0.25/n} = 0.05.[/tex]
You may be using a larger ##n## than you really need, so you may be performing more experiments than you really require. The point is, though, that you will not be performing fewer experiments than you really need.

Great response! Thanks!
 

Related to Deducing Sample Size from Sample Proportion CI

What is "Deducing Sample Size from Sample Proportion CI"?

"Deducing Sample Size from Sample Proportion CI" is a statistical method used to determine the appropriate sample size needed for a study or experiment based on the confidence interval (CI) of a sample proportion. This method takes into account the desired level of confidence and margin of error to calculate the minimum sample size needed for accurate results.

Why is it important to determine the sample size?

Determining the sample size is important because it directly impacts the accuracy and reliability of the study's findings. Having a sample size that is too small can lead to biased results, while a sample size that is too large can be a waste of resources. By using the "Deducing Sample Size from Sample Proportion CI" method, researchers can ensure that they have a sufficient sample size to support their conclusions.

What factors are considered when deducing sample size from sample proportion CI?

The main factors considered when deducing sample size from sample proportion CI are the desired level of confidence, margin of error, and the estimated or known proportion of the target population. These factors are used in the formula for calculating sample size, which takes into account the level of precision needed for the study.

Can this method be used for any type of study or experiment?

Yes, the "Deducing Sample Size from Sample Proportion CI" method can be used for any type of study or experiment that involves collecting data from a sample of a larger population. However, it is most commonly used in studies that involve collecting binary data, where the outcome can be classified as either a success or failure.

Are there any limitations to this method?

Like any statistical method, there are limitations to using "Deducing Sample Size from Sample Proportion CI". One limitation is that it assumes a normal distribution of data and may not be accurate for small sample sizes or when the proportion of interest is close to 0 or 1. Additionally, this method does not take into account any potential confounding variables that may affect the results of the study.

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