Calculating Sample Number with Central Limit Theorem for Log-Normal Distribution

[thomas49th]

Say I have a a log-normal distrubution of data. I want to use the central limit theorem to calculate how big the sample number should be. I would use the geometric standard deviation and we're dealing with a log normal distribution, correct?

Using the CLT I can arrive at the equation:

$$n = (\frac{z.\sigma}{EBM})^{2}$$

Where z is the corrisponding z score from the cofindence level. Using 95% confidence in a 2 tail test yields z to be 1.96. Sigma is the mean (it shouldn't matter if I use the geometric and oppose to the arithmetic right?) and EBM is the error bound for a population mean. Now I need some help here please, on what EBM should typically be? I'm still not sure what EBM actually is... is it the same as 'relative error'

Is this equation the right way about going to calculate the sample number required?

 

[lippyka]

Yes, the equation you have given is correct for calculating the sample size you need for a log normal distribution in order to use the Central Limit Theorem. As for the EBM (Error Bound for the Mean), it is typically set based on the acceptable margin of error that you are willing to accept in the estimate of the population mean. Generally, EBM is expressed in terms of a percentage of the population mean, and is usually set to a value between 0.05 and 0.20.