Quantifying and representing uncertainty for geothermal systems is often ignored, in practice, during the exploration phase of a geothermal development project. We propose that this occurs potentially because the task seems so formidable. The primary goal of this paper is to initiate a dialogue within the geothermal community about: which geothermal uncertainties should receive the most attention and which uncertainty analysis methods could provide the greatest benefit for the advancement of the geothermal energy industry. Specifically, in this paper, we review uncertainty quantification techniques that are applicable to geothermal exploration. In general, uncertainty associated with data acquisition/processing (i.e., objective uncertainty) is small compared to the uncertainty in interpretational space (i.e., subjective uncertainty) that lies between data points where extrapolation is required. Therefore, it is important to classify, assess, and quantify uncertainty to help select strategies to reduce uncertainty and to better gauge the impact that separate uncertainties have on the overall likelihood of project success. The discipline of geostatistics provides multiple quantitative methods for producing stochastic models which adhere to measured data and spatial correlation. The petroleum industry has successfully used both geostatistics and decision analysis methods to combine diverse and multiple types of uncertainties. We argue that instead of one single and final interpretation of the geothermal system, numerous interpretations may be more indicative of the possible subsurface scenarios, and these different scenarios can be evaluated using decision analyses and value of information methodologies. Finally, we recommend that the potential power generation of a geothermal reservoir should be grounded in the geologic data and modeling for a specific field and their estimated uncertainties.