# Confidence interval and sample size multinomial probabilities

I'm an absolute beginner in statistics. Please excuse any wrong assumptions or missing information in my question. I have a question that relates to a multinomial distribution (not even 100% sure about this) that I hope somebody can help me with. If I take a sample (lets assume $n=400$) on a categorical variable that has more than two possible outcomes (e.g. blue, black, green, yellow) and plot the frequencies so that I can derive the probabilities. E.g.: black 10% blue 25% green 35% yellow 30%

How could I compute the 95% confidence interval for those probabilities? And how could I determine the sample size required in order to get an accurate result within +-3% for each probability? Please let me know if the answer to the question requires any additional information.

• Welcome to the website, you may want to do a search on maximum likelihood estimation and standard error, this link may be a good start. P.S: Although they are talking about a different distribution (Pareto) in the link, the concepts apply to your case. – Zhubarb Aug 10 '14 at 14:00
• Also check this out: sites.stat.psu.edu/~sesa/stat504/Lecture/lec3_4up.pdf – Zhubarb Aug 10 '14 at 14:05
• Would you know how to do it if you got only two categories instead of four? – Michael M Aug 10 '14 at 14:12
• Hi Michael, I think in this case it could work with a binomial distribution and I would use the normal distribution (since it's approximately the same) to calculate the confidence interval. Please correct me if I'm wrong. – Dirk Aug 10 '14 at 16:06
• Then you can simply do this for each category separately (e.g. black vs. non-black). – Michael M Aug 10 '14 at 17:04

## 1 Answer

Thank you very much again for your help. Below is the (hopefully correct) solution using the "Normal Approximation Method" of the Binomial Confidence Interval: • For the sample size in binomial designs, sometimes one uses the 'worst' case p = 0.5 because usually the proportions are not known in advance. Further note that there are slightly better methods that the simple z approximation, e.g. Wilson's method. But your solution looks nice anyway. – Michael M Aug 11 '14 at 7:29
• Hi Michael, thanks a lot for the additional tips and the confirmation. Really appreciated. – Dirk Aug 11 '14 at 11:09
• I think there might be a mistake in the equation in the spreadsheet. In the standard error (s.e.) you need to divide by the overall sample size (n=400), not the number of realizations for each category. – user99110 Dec 26 '15 at 17:15