Use of confidence intervals to demonstrate performance against forest management standards

Abstract

The objective of continuous improvement embedded in forest management standards relies on the capacity of management to respond appropriately to evidence of performance provided by monitoring. This evidence is rarely unequivocal. Under a null hypothesis of no effect, two kinds of errors in interpretation are possible—inferring an effect where none exists (Type I error) and inferring no effect when in fact one exists (Type II error). If the monitoring relates to possible improvement in growth or yield then a Type I error leads to false optimism and a Type II error to false pessimism. If monitoring concerns a potential environmental or social impact, a Type I error implies alarmism and a Type II error a false sense of security. Explicit consideration of statistical power in designing and interpreting monitoring data is an effective buffer against these errors. However, strict application of statistical power may be impractical. In particular, the requirement to specify tolerable error rates and effect sizes will be difficult in many circumstances where the perspectives of managers, auditors or stakeholders are contested or perceived to be arbitrary or vague. We advocate the use of confidence intervals as an alternative to power calculations. Confidence intervals offer an accessible approach to communicating performance under a standard and the extent to which a monitoring program is able to distinguish compliance from non-compliance. We illustrate these arguments and tools through a hypothetical example involving a proposed change in silviculture where the magnitude of gains in yield and environmental impacts are unclear.