Mathematical equations are available to estimate the number of samples required to produce a reliable estimate of a population parameter. Determining the optimum number of samples needed should be an initial step of every species inventory, regardless of whether it is to determine presence or abundance. More sophisticated studies may not only attempt to estimate a parameter's size, but to detect changes in it over time or space. Statistical estimates of sampling effort required to detect changes or trends tend to focus on the concept of statistical power. Additionally, the relationships between these parameters depends on the ecological process producing the trend and the techniques used to detect it.
Power analysis is useful in two respects.
First an a priori power analysis can provide guidance to ensure an adequate sample size. This exercise will help to prevent the implementation of a study which is too weak and unable to discriminate a meaningful difference. Secondly, a retrospective power analysis is also useful in assisting interpretation of study results. For example, a study objective might be to detect a decline in a sensitive species in an area receiving a certain forest-cutting practice. Failure to reject the H o provides evidence that forest-cutting is having no effect on this sensitive species.