What is a common reason analysts produce differing results in an analysis?

Differing results in an analysis often stem from the use of different assumptions throughout the process. Assumptions are foundational beliefs or conditions that analysts establish at the beginning of an analysis to guide their work. These can include perspectives on potential threat actors, the likelihood of certain events occurring, or the impact of those events. Since analysts may have varying insights, perspectives, or experiences that lead to divergent assumptions, it is not uncommon for these differences to result in significant variations in the final outcomes of their analyses.

When assumptions are made, they can directly influence calculations of risk, the selection of variables, and the interpretation of data. As a result, even when working with the same dataset, different analysts can arrive at markedly different conclusions because their underlying assumptions may support different interpretations of that data.

While interpretations of taxonomy, analyst experience, and data quality can influence results to an extent, the most significant and foundational differences typically arise from assumptions, which shape the entire analytical framework. This aspect makes it essential for analysts to clearly document and communicate their assumptions during the analysis process to avoid misunderstandings and facilitate more consistent outcomes.