The work of the intelligence analyst is conducted within the realm of uncertainty and with the aim of reducing the veil of uncertainty through which judgments, decisions and actions must be taken. Since few inferences in the dynamic, complex world of decision-making lend themselves to the rigor of statistical analysis, most of the objective, mathematical approaches to the assessment of uncertainty are not applicable. Thus, in assessing and communicating the level of confidence that should be associated with a specific inference, the analyst must employ subjective conditional probabilities. That is, not only must critical thinking skills be employed to assemble evidence, generate premises and develop an inference, they must also be employed to arrive at the level of confidence one should have in the inference (Klein et al., 2006).
Moreover, the analyst is faced with a tradeoff between the level of detail in an inference (the answers to who, what, when, where, why and how questions) and the level of confidence that can be given to the inference. More detail provides a more useful inference but typically at the sacrifice of confidence; less detail provides a greater level of confidence but typically at the sacrifice of usefulness. One of the challenges faced by the analyst is to make an effective tradeoff between detail and confidence.