The Minimum Detectable Effect (MDE) is the smallest change in a metric that an experiment can reliably detect. For instance, if the MDE is 1%, it means that if there's a true effect of 1% or larger on our metric, we expect the experiment will show a statistically significant result. If the effect is smaller than 1%, then it will likely fall inside the confidence intervals and not be statistically significant.
The MDE is influenced by several factors, including the number of days or exposures (how long the experiment is active and the number of users enrolled in it), and the allocation (the percentage of traffic that participates in the experiment).
In terms of using the power analysis tool, you can select the population used to determine the metric mean and variance and to estimate the number of exposures over time. The population types include everyone (analysis is based on the entire user base), targeting gate (analysis is scoped to the set of users that pass the selected feature gate), and past experiment (analysis is based on data collected from in a previous experiment).
The tool also allows for fixed allocation analysis (to understand how the length of the experiment impacts the MDE) and fixed MDE analysis (if the smallest effect size that the experiment should detect is known).
Remember, the population selected directly impacts the inputs of the analysis (mean, variance, number of users). To obtain reliable power analysis estimates, the metric values of the selected population should roughly match those of the users you'll be targeting in the experiment.