Planning a monitoring project requires careful planning. The inference space of a project must be balanced with the sampling requirements. Inference space is the population from which the samples in a study were drawn and the population to which results of a study apply. Increasing the number of conditions in a project increases the inference space. Sampling requirements are the number of samples or measurements required to detect a given level of change. The number of samples required to detect change depends on the variability of the conditions being evaluated. In general, the fewer conditions that are sampled the lower the variability that will be observed and the lower the measurements required to detect change. Thus increasing the inference space is usually in direct conflict with reducing sampling requirements. This is a problem when available resources allow only a limited number of measurements. The objective of this study is to explore the balance between detectable change, inference space, and sampling requirements for a dataset of soil and range measurements. We use resampling procedures to show these relationships for a dataset with 4 ecosites and 2 degradation conditions. Variability (measured as variance) increases as the number of ecosites increases, however, some ecosites add more variance than others. Limiting the number of ecosites and conditions can reduce the number of samples or measurements required to detect change.