While Table 1 lists the minimum change that could be associated with biologically relevant endpoints, other field studies have reported much higher changes in observed parameters. For example, populations of white sucker (Catostomus commersoni) exposed to bleached kraft mill effluents had GSI, LSI and CF deviations of 30% or more relative to reference fish ( Mower et al., 2011). The power of the test, 1-β, is a third factor influencing the Selleckchem Trichostatin A number of samples to collect. The convention in environmental sciences is
that power should be at least 0.80 ( Fairweather, 1991), i.e., there should be an 80% chance of detecting a difference between sites. The power of a test can be determined easily from calculations
using similar variables as the minimum sample size (G∗Power 3 can calculate power using a different set of instructions). Obviously, collecting the minimum number of samples will give low power and increase the chances of committing a Type II error (false negative: concluding there is no impact when in fact there was one). In a multi-sample analysis of variance, the power increases rapidly with the number of samples used. Consequently, if there is an opportunity to collect a few more fish at each site, the benefit of each additional fish can be calculated using the power equations. In the present case, the n required ubiquitin-Proteasome system has been calculated for a power of 0.80 and 0.95, as under many situations it is prudent to reduce the possibility of Type II error where possible. From the perspective of environmental management, a Type II error is far more serious than a Type I error. A Type I error can be seen as a false alarm which could trigger further environmental protective measures – it is only a question of time before the mistake is realized through additional sampling. In contrast, a Type II error leading to a conclusion of ‘no impact’ would result in no remediation measures being implemented, a possible
reduction in monitoring effort, and a continuing environmental deterioration. Thus, due to a lack of statistical power, there would be continued environmental degradation. The fourth factor affecting the minimum required sample size is Interleukin-3 receptor the variability of the parameter. Biomarkers can be notoriously variable. For example, the coefficients of variation of all parameters except CF ranged from 12.6% to 127% (Table 2), while the coefficient of variation for CF averaged 6.1%. If the variability within a sampling site is great, a larger sample size will be required to detect a given difference between means (Zar, 1996). Sources of variability for a given biomarker include individual (random) variability, systematic sampling error due to confounding factors, and analytical variability.