Technical Abstract: Renewed interest in quantifying greenhouse gas emissions from soil has lead to an increase in the application of chamber-based flux measurement techniques. Despite the apparent conceptual simplicity of chamber-based methods, nuances in chamber design, deployment, and data analyses can have marked effects on the quality of the flux data derived. In many cases fluxes are calculated from chamber headspace vs. time data sets consisting of 3 or 4 points. Several mathematical techniques have been used to calculate a soil gas flux from time course data. This paper explores the influences of sampling and analytical variability associated with trace gas concentration quantification on the flux estimated by linear and nonlinear models. Specifically, we used Monte Carlo simulation to calculate the minimum detectable fluxes (a = 0.05) of linear regression (LR), the Hutchinson/Mosier method (H/M), the quadratic method (QUAD), the HMR model, and restricted versions of the Quad and H/M methods over a range of analytical precisions and chamber deployment times for data sets consisting of 3 or 4 time points. We found that linear regression had the smallest detection limit thresholds and was the least sensitive to analytical precision and chamber deployment time. The HMR model had the highest detection limits and was most sensitive to analytical precision and chamber deployment time. Equations were developed which enable the calculation of the flux detection limits of any gas species if the analytical precision, the chamber deployment time, and the ambient concentration of the gas species are known.