Little attention has been given to optimizing statistical power for monitoring stream fish assemblages. We explored the relationship between temporal variability and statistical power using 34 metrics from fish community data collected annually at six sites over 10 years via electrofishing. Metric variability differed by the life stage and group of species considered, use of abundance or mass data, and data standardization technique. Lower variability was associated with community data, abundance data, and time-based standardizations, while greater variability was associated with young-of-the-year data, mass data, and area-based standardizations. Simulation-based power analysis indicated metric choice, and to a lesser degree, monitoring design (annual, biennial, endpoints, or haphazard sampling) influenced power to detect change. Across a fixed number of surveys (N = 60), endpoints sampling performed best. The N needed to detect change was heavily dependent upon metric choice for all monitoring designs, with the most biologically specific metrics requiring greater N. Large savings in effort and resource expenditure can be obtained utilizing biologically relevant metrics that are robust to temporal noise within an appropriate sampling design.