Ecological studies relying on complex calculations, such as ecosystem budgets, often report results without any indication of uncertainty, which makes it difficult to evaluate the significance of findings or make comparisons across systems. We present a Monte Carlo approach to estimating uncertainty in key ecological fluxes and pools such as nutrient inputs in precipitation, outputs in streamflow, and stores in vegetation, using data from the Hubbard Brook Experimental Forest in New Hampshire. We have examples of Monte Carlo analyses implemented in Excel, R, and SAS (http://www.esf.edu/for/yanai/currentresearch.htm). Participants should bring laptop computers and ecological data and calculations in need of uncertainty analysis; you can use our data if you don't have your own. At the end of the workshop, some participants will have documented the uncertainty in their result. All participants will understand the principles of Monte Carlo sampling and will have tools for implementing uncertainty analyses.