There are unresolved issues surrounding the use of power analyses for examining the ability of CEMP data to detect change. The effect size that should prompt a response if observed in a parameter and the likely response of parameters such as arrival or fledgling weights are two of these issues. Understanding the source of the variability required to generate ‘noise’ for the power analysis simulations is another and it has major ramifications on power estimates. Increasing the variability associated with power analysis estimates results in a decreased level of power to detect a trend. Similar power analysis results are obtained for a fixed co-efficient of variation of the temporal variability SD in relation to the initial value irrespective of the magnitude of the initial size. This means that power analysis results based on occupied nest counts with increasing levels of temporal variability (% CV) are applicable to other parameters that are suitable for trend detection. The four CEMP parameters considered in this paper have similar power estimates because their estimates of temporal variability are comparable (5.2 – 6.7%). For example, with a 10 year monitoring program it is possible to detect fixed increases or decreases larger than 2% each year with more than 80% power. Increasing the duration of the monitoring program has a positive impact on power estimates. There is very little difference between results generated using an exponential model compared with a linear model for short term monitoring programs of up to 10 years duration.