**Prasanta Chandra Mahalanobis — The Early Years**

Precocity is overrated. We have come to overvalue this trait in students because of the hyper-competitive age we live in. It is true that many exceptionally smart people do show early signs of genius, but it is also equally likely for remarkable individuals to have been thoroughly unremarkable in their youth. Mahalanobis was amongst the latter. He had quite an uneventful stint at Presidency College, Calcutta, other than the fact that he might have been taught by J. C. Bose, and that, Netaji Subhas Chandra Bose was two years junior to him. At Cambridge, he interacted with Srinivasa Ramanujan. Ramanujan left anecdotes with everyone he met, and Mahalanobis recounts a very interesting one here. Nevertheless, there still was no foreshadowing. On his voyage back to India, he is known to have read all nine volumes of Biometrika,

a revered statistical journal which counted Karl Pearson among its founders. While there was no precocity, Mahalanobis certainly did not lack tenacity and determination. After discovering the applications of statistical techniques to meteorology and anthropology, he would work on these problems for the greater part of his life, particularly in sampling and multivariate analysis.

**David and Goliath**

One of the characteristics of successful scientists is having courage. Once you get your courage up and believe that you can do important problems, then you can. If you think you can’t, almost surely you are not going to. Courage is one of the things that Shannon had supremely. You have only to think of his major theorem. He wants to create a method of coding, but he doesn’t know what to do so he makes a random code. Then he is stuck. And then he asks the impossible question, “What would the average random code do?’’ He then proves that the average code is arbitrarily good, and that therefore there must be at least one good code. Who but a man of infinite courage could have dared to think those thoughts?

— Richard W Hamming *(You and Your Research**)*

In 1923, Mahalanobis wrote a manuscript titled “On the Seat of Activity in the Upper Air”. It was a critique of the work of William Henry Dines, an independently wealthy English meteorologist who had invented the pressure tube anemometer. It was a device that could be mounted under a hot air balloon and when deployed, it would fly around with the balloon, recording air pressures at various altitudes. Following his experiments with the device, Dines inferred that the pressure at a height of 9 kilometres (the so-called “seat of activity”) from sea-level is what affects pressure changes throughout earth’s atmosphere to the greatest degree. Mahalanobis examined Dines data from a rigorous statistical standpoint, and ended up estimating that the layer of atmosphere from the height of 2 km to 4 km was statistically more telling, than the 9 km estimate reported by Dines. Mahalanobis’ primary contention was that Dines had ignored sequential measurements when the anemometer would ascend with the balloon, thereby, leading to a false statistical correlation.

The manuscript did get published in the Memoirs of the Indian Meteorological Department, but was quite negatively criticised by Dines himself in the 1923 issue of *Nature.*

In response to Dines’ letter, Mahalanobis said that Dines had still not addressed the original issue. It is difficult to tell if the feud was ever settled, but Mahalanobis’ apparent “effrontery” stands out prominently in this whole affair. He did not know that he was not supposed to take on a titan like Dines.

**Collaboration with Fisher**

The single longest collaboration of Mahalanobis’ life was with Ronald Fisher (creator, among other things, of the immensely famous Iris flower dataset). Mahalanobis and Fisher were at Cambridge at somewhat the same time, but strangely they never ran into each other. They did correspond frequently.

Mahalanobis’ initial work on meteorological data had a great impact in studies related to weather, rainfall, soil conditions and ultimately agriculture. Statistics was being recognised in India as a key discipline within anthropological studies as well. Mahalanobis’ work in anthropological data lead to methods that could be applied to the classification of populations characterised by anthropological measurements. This was called the Mahalanobis Distance (it is essentially a way of measuring the distance between a fixed point and a set of points, defined as a statistical distribution).

In the November of 1929, Mahalanobis submitted this work to the coveted *Biometrika*, but Karl Pearson (the founder of the journal) rejected his submission without comment. Mahalanobis wrote to Fisher about this, and the latter sympathised, recommending that Mahalanobis make the submission to the journal of the Royal Anthropological Society. However, the Royal Anthropological Society too rejected the submission because they thought it was being considered for publication in the *Biometrika*.

When Fisher found out about this, he thought Mahalanobis and *Biometrika *had struck a deal behind his back (he was known to be a touchy person, and would often be easily offended). When confronted, Mahalanobis sent a five-page reply, explaining that he had actually sent two papers to the *Biometrika *both at the same time. Pearson had rejected only one of them, and had asked for an abridged version of the second. By ‘abridged’, he meant that the Biometrika would only publish the data from the second paper, not the analysis. As far as Fisher was concerned, Mahalanobis had lied by omission, and suggested that Mahalanobis continue entirely on his own without involving Fisher.

Stephen Stigler shows how Fisher’s salutations in letters to Mahalanobis changed over the years.

Fisher and Mahalanobis had their ups and downs, but their partnership was to become a cornerstone of modern statistics.

**The Fisher Lectures**

The Indian Statistical Institute was founded in a small classroom in Presidency College in 1931. At this time, it was barely more than a society. Early sessions of the society consisted mostly of Mahalanobis delivering talks on his work and various related subjects. *Sankhya*, the Indian journal of statistics, was founded two years later. The first Indian Statistics Conference was held in 1938, with none other than Ronald Fisher presiding. The then Viceroy of India was also in attendance.

Over the coming years, Fisher visited India no less than eight times, and his lectures during these visits are the stuff of legend. He spoke about the theory of estimation, a topic on which he had authored many papers, the best known of which is *The Mathematical Foundations of Theoretical Statistics *(A very readable summary of Fisher’s work in the early 20s has been published by Stigler). He also lectured about the design of experiments, and the theory of genetics — a topic that had captured the imagination of the best statisticians of the time.

Fisher saw these lectures as a change to sum up his life’s work. But he was known to be a bad lecturer. When asked by an unsuspecting student to use better words, he famously said, “Young man, these *are t*he best words!” Mahalanobis, however, was very well prepared for this. He made sure that his students (most prominently R C Bose and K R Nair) had done their homework with Fisher’s work well before the lectures happened. They would spends months studying the lecture material before the lectures. The collection of these lectures was later turned into a book published by the University of Calcutta, which is one of the most definitive texts in modern statistics.

**The Building of an Enterprise**

The true contribution of Mahalanobis is that he was able to, immensely successfully, bring together a confluence of people and ideas. Because of this alone, he succeeded on a grander scale, and with many more challenges, than Pearson or Fisher did. In his book, *The Seven Pillars of Statistical Wisdom*, Stigler writes,

With all the variety of statistical questions, approaches and interpretations, is there then no core science of statistics? If we are fundamentally dedicated to working in so many different science, from public policy to validating the discovery of the Higgs boson, and we are sometimes seen as mere service personnel, can we really be seen in any reasonable sense as a unified discipline, even as a science of our own?

This is an identity crisis that many statisticians must have suffered. Today, statisticians are indispensable. The AI revolution, at its core, is a victory of statistical analysis. Indeed, mathematical statistics was once viewed simply as a service. Remember that Mahalanobis’s contribution to *Biometrika* was valued only for its data, not its analysis. We have come a long way since then, and few people can claim to be as instrumental to the development of statistics into a global enterprise as Prasanta Chandra Mahalanobis.