# Umbrella term for issues when not having enough data to properly do a test.

Say I want to make a specific test. What is the correct terminology to describe the issue of having enough data to properly do it. Consider for instance:

"Of course the authors also needed to consider the issue of including enough data in order for their tests to be/have -INSERT WORD-".

One could of course say have the correct power/size, but this is specific issues so I guess I am looking for a kind of umbrella term meaning: without all the types of problems one encounters when the sample is too small.

• Reliable perhaps? – JonB Sep 11 '15 at 10:07
• @JonasBerge I actually like this, albeit I was expecting a statistical term. – Henrik Sep 11 '15 at 11:55
• I don't think there is one that can always be used. – JonB Sep 11 '15 at 11:56
• 'Reliable' is a test-retest concept, which is perhaps not what you are looking for. As per my comment to @tim, the relevant concept is indeed 'power' via 'estimation precision'. – conjugateprior Sep 11 '15 at 12:05

• I think @tim is correct that power is the right concept. Consider that, in addition to the model specification, power is a function of desired $\alpha$, effect size, and sample size. While it's traditional to fix model, $\alpha=0.05$ (or somesuch), and expected effect size, then compute a required sample size, the concept can be used other ways. Specifically, desired $\alpha$ is tightly connected to confidence interval width. So requiring a smaller $\alpha$ in the previous power computation indirectly demands a more precise i.e. narrower, intervals, which is what you want. – conjugateprior Sep 11 '15 at 12:01