Give me Facts, Not Data: Florence Nightingale’s Pursuit of Truth in India
64th ISI World Statistics Congress - Ottawa, Canada
Format: CPS Abstract
Keywords: data and information’s
Session: CPS 53 - Teaching statistics III
Tuesday 18 July 4 p.m. - 5:25 p.m. (Canada/Eastern)
Florence Nightingale’s pioneering contributions in the collection and analysis of statistical data to support evidence-based decision making for the public good is well known. What remains less known, and thus unacknowledged by the statistics profession, is the priority Nightingale accorded to the collection of “facts”, or observed reality, over data and statistics. In her later years, Nightingale undertook a remarkable correspondence project with a young Indian lawyer in an attempt to gather “facts” – as opposed to data - on the state of tenant farmers and tenancy reforms in late-nineteenth century British India. Their correspondence, sustained over a four-year period, between 1878 to 1882, was published later as her “Indian Letters”. We explore these letters to clarify the Nightingale distinction between facts and data. Her skepticism of data/statistics and her insistence on facts in the above case was partly motivated by her belief that the latter, more than the former, would be needed to convince her British audience, including the Parliament who she undertook to lobby on behalf of the Indian peasants suffering under the existing set of land and tenancy rights in Bengal. We argue, however, that the Nightingale distinction and her skepticism of data reveal insights that have remained relevant for both statistical practice and policy. We point to the overestimation of Soviet growth data and potential that continued to plague the West’s assessment of the USSR as late as the 1980s. In addition, we provide insights from our more recent attempts to assess the quality of data used by the Government of Myanmar against the “facts” that we observed in the field. Finally, we argue that Nightingale-facts are an important check against the inherent limitations of data collection, modelling biases, and their use to inform public policy.