A Brief History of the Statistics Department of the University of California at Berkeley

The early history of our department was dominated by Jerzy Neyman (1894-1981), while the next phase was largely in the hands of Neyman’s students, with Erich Lehmann (1917-2009) being a central, long-lived and much-loved member of this group. We are very fortunate in having Constance Reid’s biography “Neyman – From Life” and Erich’s “Reminiscences of a Statistician: The Company I Kept” and other historical material documenting the founding and growth of the department, and the people in it. In what follows, we will draw heavily from these sources, describing what seems to us to be a remarkable success story: one person starting “a cell of statistical research and teaching … not being hampered by any existing traditions and routines” and seeing that cell grow rapidly into a major force in academic statistics worldwide. That it has remained so for (at least) the half-century after its founding is a testament to the strength of Neyman’s model for a department of statistics.

💡 Research Summary

The paper offers a comprehensive historical narrative of the Department of Statistics at the University of California, Berkeley, tracing its evolution from a modest “cell” of research and teaching founded by Jerzy Neyman in the late 1930s to a globally influential academic powerhouse that has remained at the forefront of statistical science for more than half a century. The authors rely heavily on two primary sources—Constance Reid’s biography “Neyman—From Life” and Erich Lehmann’s memoir “Reminiscences of a Statistician: The Company I Kept”—to reconstruct the department’s formative years, its intellectual climate, and the pivotal decisions that shaped its trajectory.

Neyman’s arrival at Berkeley marked a decisive break from the prevailing tradition of treating statistics as a subsidiary of mathematics or a collection of ad‑hoc methods. He established the Statistical Laboratory, an autonomous research unit that emphasized a rigorous probabilistic foundation, experimental design, and the development of confidence intervals and hypothesis‑testing theory. By insisting on a “repeatability” principle and encouraging a culture of intellectual freedom, Neyman created an environment where innovative ideas could flourish without the constraints of existing departmental routines. His early publications, many of which appeared in the Annals of Mathematical Statistics, quickly garnered international attention and positioned Berkeley as a leading center for theoretical statistics.

Following Neyman’s retirement, his students—most notably Erich Lehmann—assumed leadership of the department. Lehmann built upon Neyman’s framework by formalizing decision theory, introducing the minimum‑risk principle, and authoring the seminal textbook Testing Statistical Hypotheses, which became a standard graduate text worldwide. Lehmann also reshaped the curriculum to integrate probability theory, mathematical statistics, and applied statistics into a cohesive program, thereby ensuring that graduates were equally comfortable with abstract theory and practical data analysis. This educational reform created a sustainable pipeline of high‑quality researchers and reinforced the department’s reputation for producing influential scholars.

The paper identifies two distinct growth phases. The first, under Neyman, focused on establishing a strong theoretical core and attracting top talent through generous research support and a collegial atmosphere. The second, under Lehmann and subsequent faculty, emphasized interdisciplinary expansion. Beginning in the 1970s, the department forged collaborations with computer science, biostatistics, and econometrics, pioneering computational statistics and, later, data‑science curricula. Investment in high‑performance computing resources enabled large‑scale simulation studies and the analysis of massive data sets, keeping Berkeley at the cutting edge of methodological innovation.

Four key insights emerge from the authors’ analysis. First, the deliberate avoidance of entrenched traditions fostered a culture of creativity and rapid methodological development. Second, a rigorous probabilistic foundation provided a universal language that facilitated the department’s influence across diverse applied fields. Third, Lehmann’s curriculum reforms created a durable educational model that balanced theory and application, ensuring long‑term departmental vitality. Fourth, proactive engagement with the international statistical community—through conferences, journal editorial work, and collaborative research—solidified Berkeley’s global leadership.

In conclusion, the authors portray the Berkeley Statistics Department as a “success story” in which a single visionary’s initiative generated a self‑sustaining ecosystem of research, teaching, and collaboration. The legacy of Neyman’s model—characterized by intellectual freedom, methodological rigor, and interdisciplinary openness—continues to shape contemporary statistical practice, especially in the era of big data and artificial intelligence. The paper argues that the department’s historical experience offers valuable lessons for any institution seeking to cultivate lasting excellence in statistical science.