The central limit theorem says that the sampling distribution of the mean will always be normally distributed, as long as the sample size is large enough. Regardless of whether the population has a normal, Poisson, binomial, or any other distribution, the sampling distribution of the mean will be normal.
For continued process verification (CPV) or other statistical analysis, we assume that n=30 or more will ensure normal distribution and this "n" (sample number) is generally represented by the number of batches. For example, assay of an API (active pharmaceutical ingredient) in a drug product from 30 different batches is considered normally distributed. According to above definition, the assay of each batch has to be calculated mean of adequate number of samples (sample size) for each and every batch and not the composite sample assay or assay of one tablet or capsule. Hence the assumption of normal distribution on the basis mean coming from one individual assay is not right. In this regard, dissolution can be considered better candidate for the application of central limit theorem. For each mean value of dissolution time point, at least we use 6 units or more (L2, L3). Any comment will be appreciated.
