This dissertation offers a political history of the cultural trope and technical apparatus: ‘with 95% certainty,’ and of uncertainty more broadly, from the early 1920s mathematical statistics movement through the design of FORTRAN and ALGOL language digital algorithms of the 1960s and 1970s. The work features a prominent twentieth-century data architecture: confidence interval parameters (CIs). Confidence intervals are statistical hypothesis tests, and experimental design mechanisms, used to make estimations about statistical data, and inform subsequent decision-making based on that information and analysis. CIs connect across digital and predigital computing and function as part of the underpinning logical and political infrastructures that make algorithmic decision-making possible. I situate digital algorithms and statistical hypothesis tests as common ‘data architectures,’ that operate under uncertainty (probabilistic thinking), and that are designed to make certainty claims (political decisions) based on a set of information. By the 1960s, digital algorithms were designed to take over the (un)certainty work of human computers.

Dryer, T. (2019). Designing Certainty: The Rise of Algorithmic Computing in an Age of Anxiety 1920-1970. UC San Diego. ProQuest ID: Dryer_ucsd_0033D_18608. Merritt ID: ark:/13030/m5382d0s. Retrieved from https://escholarship.org/uc/item/4d02g6x3