Bioequivalence and Bioavailability Forum

Hi El Maestro,

Statistical classes define the standard error as the variability of some estimated parameter, such as the mean, odds ratio, proportions, etc. I'm pretty sure this is not what you're looking for, but imo the definition is intuitive especially when you think at the population level. We are working with samples and we know that when we analyse a sample we are only obtaining estimates of the "true" population parameters. If you conduct 3 or 4 bioequivalence studies with the same product under the same conditions you will obtain 3/4 different estimates of the T/R ratio since you used different samples. The standard error is like the "standard deviation" of those estimates. Since we don't conduct lots of studies to estimate a single parameter, we generally obtain the standard error from the standard deviation of the sample. We assume that samples with higher variability will yield parameters with higher variability. The other assumption is regading the sample size. We also assume that higher samples produce estimates with a higher degree of uncertainty regading the population - and therefore with lower standard errors. This is in some way the principal concept of frequentist statistics. The concept of obtaining the variability of the parameter from the variability of the sample. The nicest thing about the standard error is that in general the "parameter distribution" (either a mean, relative risk, odds ratio, etc) will be normal and will let us calculate a "confidence interval" based on the standard error to obtain an estimate of how accurate our estimation process was.

I think that a great way to understand the concept of standard error is to learn the bootstrap techniques. Bootstrapping is in fact using the "frequentist theory" in the world of simulation, including the concept of standar errors to estimate parameters.

Not sure if this helped with your question, since you may already know all of this. I hope the bootstrap may be a better teacher than I am