Statisticians
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Develop or apply mathematical or statistical theory and methods to collect, organize, interpret, and summarize numerical data to provide usable information. May specialize in fields such as biostatistics, agricultural statistics, business statistics, or economic statistics. Includes mathematical and survey statisticians.
The occupation of "Statisticians" has an automation risk of 53.1%, which is slightly below the base risk of 53.9%. This moderate risk reflects the dual nature of the tasks performed in this field—many analytical and technical components are increasingly performed by advanced software and artificial intelligence systems. The tasks most susceptible to automation include analyzing and interpreting statistical data to highlight significant relationships, evaluating the statistical methods and procedures used to ensure their validity and efficiency, and reporting statistical findings via graphs, charts, and tables. These tasks often involve structured data manipulation, repetitive pattern recognition, and standardized reporting, all of which are domains where automation technologies like machine learning and data visualization tools excel. Despite the considerable automation risk, several critical tasks remain resistant due to their inherent need for human judgment, creativity, and oversight. Among the least automatable tasks are preparing and structuring data warehouses for data storage—a process that often requires comprehensive understanding of the organization’s unique needs and goals. Supervising and providing instructions to workers collecting and tabulating data also relies on people-oriented leadership and adaptability, traits that are not easily mimicked by automation. Furthermore, examining complex statistical theories and developing new methods of inference require innovation, abstract reasoning, and deep domain expertise, all of which present significant challenges for AI systems to replicate at a high level. Bottleneck skills that help mitigate the overall automation risk for statisticians are strongly related to originality, with levels reported at 3.0% and 3.6%. Originality involves the ability to generate new ideas, approaches, or interpretations—an area where artificial intelligence still falls short compared to human professionals. Statisticians frequently face novel problems and ambiguous datasets that demand creative approaches for meaningful analysis and decision-making. The need for innovative solutions, especially when dealing with complex or unstructured data, acts as a significant barrier to full automation. As a result, while routine statistical tasks may become increasingly automated, the occupation as a whole will continue to require human statisticians to drive original research and adapt to new analytical challenges.