Signatories of “Letter A”, titled “ The math community values a commitment to diversity “, “strongly disagreed with the sentiments and arguments of Dr. The response letters range from strong affirmation of Thompson’s opinions, to strong refutation of them. Thompson’s letter compared diversity statement requirements of job applicants to loyalty oaths required during McCarthyism. The preprint analyzes data on signatories of three letters submitted in response to an opinion piece on diversity statement requirements for job applicants published by Abigail Thompson, chair of the mathematics department at UC Davis. I applaud the authors for being fully transparent and making available all of their code and data in a Github repository in a form that made it easy to reproduce all of their results indeed I was able to do so without any problems. In the case of a preprint such as this one, this means having access to the code and data used to produce the figures and to perform the calculations. In order to assess the results of any preprint or paper, it is essential, as a first step, to be able to reproduce the analysis and results. I was recently asked to provide feedback on the manuscript, ergo this blog post. This statistics preprint examines attempts to identify the defining attributes of mathematicians who signed recent letters related to diversity statement requirements in mathematics job searches. Joshua Paik and Igor Rivin, Data Analysis of the Responses to Professor Abigail Thompson’s Statement on Mandatory Diversity Statements, arXiv, 2020. Case in point is a recent preprint by two mathematicians: A recent debate on the appropriateness of diversity statements for job applicants in mathematics highlights the need: analysis of data, specifically data on who is in the maths community, and their opinions on the issue, turns out to be central to understanding the matter at hand. The divide between mathematicians and statistics is unfortunate for a number of reasons, one of them being that statistical literacy is important even for the purest of the pure mathematicians. This dynamic was later repeated at universities across the United States, resulting in a large gulf between mathematicians and statistics (ironically history may be repeating itself with some now suggesting that the emergence of “data science” is a result of conservatism among statisticians leading them to cling to theory rather than to care about data). However, Evans’ progressive vision for mathematics was not shared by all of his colleagues, and the conservative, parochial attitudes of the math department contributed to Neyman’s breakaway and eventual founding of the statistics department. Evans’ vision for the Berkeley math department included statistics, and Eric Lehmann‘s history of the UC Berkeley statistics department details how Evans’ commitment to diverse areas in the department led him to hire Neyman without even meeting him. Neyman was hired in the mathematics department at UC Berkeley by a visionary chair, Griffith Evans, who transformed the UC Berkeley math department into a world-class institution after his hiring in 1934. Hotelling’s lecture on “ The place of statistics in the university” inspired the creation of several statistics departments, and at UC Berkeley, Neyman’s establishment of the statistics department in the 1950s was a landmark moment for statistics in the 20th century. The symposium, organized by Berkeley statistician Jerzy Neyman, was the first of six such symposia that took place every five years, and became the most influential meetings in statistics of their time. The widespread establishment of statistics departments in the United States during the mid-20th century can be traced to a presentation by Harold Hotelling in the Berkeley Symposium on Mathematical Statistics and Probability in 1945.
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