Definition and overview of the hypothesis formalization steps and process. Hypothesis formalization is a dual-search process of translating a conceptual hypothesis into a statistical model implementation. Blue indicates steps and transitions that we identified. Black indicates steps and transitions discussed in prior work. “Mathematical Equation” (dashed box) was rarely an explicit step in our lab study but evident in our content analysis. Our findings (blue arrows) corroborate and subsume several of the transitions identified in prior work with greater granularity. When they do not, prior work’s transitions are included in black. For example, analysts may operationalize a conceptual hypothesis as a causal model (found in prior work, see Figure 2) by first decomposing the conceptual hypothesis into sub-hypotheses and then identifying proxy variables to incorporate in a causal model (blue arrows above). Our definition of hypothesis formalization is a consequence of our synthesis of prior work, content analysis, lab study, and analysis of tools. Hypothesis formalization is a non-linear process. Analysts iterate over conceptual steps to refine their hypothesis in a hypothesis refinement loop. Analysts also iterate over computational and implementation steps in a model implementation loop. Data collection and data properties may also prompt conceptual revisions and influence statistical model implementation. As analysts move toward model implementation, they increasingly rely on software tools, gain specificity, and create intermediate artifacts along the way (e.g., causal models, observations about data, etc.).