Key Highlights
- Mahalanobis pioneered modern budgeting and statistical practice during the colonial era.
- He founded the Indian Statistical Institute (ISI) in 1932, creating a hub for quantitative research.
- The Mahalanobis Distance remains a cornerstone technique in multivariate analysis.
- His planning model guided India’s Second Five‑Year Plan, accelerating industrial growth.
- National Statistics Day (June 29) commemorates his enduring legacy.
Detailed Insights
Born in 1893 in Calcutta, Prasanta Chandra Mahalanobis excelled academically, ultimately securing a physics degree from Cambridge where he collaborated with eminent scholars such as Srinivasa Ramanujan. Upon returning to India, he recognised the dire need for systematic data collection and quantitative decision‑making. In 1932 he established the Indian Statistical Institute, an institution that quickly became the nation’s premier centre for statistical theory, large‑scale survey design, and applied research.
Mahalanobis introduced a metric now bearing his name—the Mahalanobis Distance— which quantifies the separation between an observation and a multivariate distribution, accounting for correlations among variables. This method revolutionised disciplines ranging from anthropology to environmental science, enabling more reliable clustering and classification.
Beyond pure statistics, Mahalanobis played a decisive role in economic planning. As a member of the Planning Commission, he authored the Mahalanobis Model, a framework that prioritized heavy industry and capital formation in the Second Five‑Year Plan (1956‑1961). His advocacy for expansive sample surveys supplied the empirical foundation for policy formulation, covering topics such as consumer expenditure, agricultural yields, and public opinion.
Throughout his career he earned numerous distinctions, including the Padma Vibhushan (1968) and fellowship of the Royal Society (1945). Posthumously, the Indian government instituted National Statistics Day on his birthday, underscoring his lasting impact on both national policy and global statistical practice.