last highlighted date: 2024-07-12
Highlights
- Guinness was unusually focused on using science to improve its products. They hired the “brightest young men they could find” as scientists, and gave them liberal license to innovate and implement their findings. Perhaps the equivalent of being a computer scientist at Bell Labs in the 1970s or an artificial intelligence researcher at Google today, it was a wonderful job for the inquisitive and practical minded Gosset.
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- Between 1887 and 1914, the output of the brewery doubled, reaching almost one billion pints. How could the company increase production, while keeping its beer tasting as consumers expected? Gosset was assigned as part of the team that would answer that question.
- After a year spent on sabbatical at Pearson’s lab, Gosset had worked out the math behind a “law of errors” when working with small samples. Today, we know his discovery as the “Student’s t-distribution”. It is the primary way to understand the likely error of an estimate depending on your sample size and remains highly depended upon by those in academia and industry.
- Upon completing his work on the t-distribution, Gosset was eager to make his work public. It was an important finding, and one he wanted to share with the wider world. The managers of Guinness were not so keen on this. They realized they had an advantage over the competition by using this method, and were not excited about relinquishing that leg up. If Gosset were to publish the paper, other breweries would be on to them.
- So Gosset published his article introducing the t-distribution, “The Probable Error of the Mean”, under the name “Student.” “The Probable Error of the Mean” is a relatively dry piece of work, mostly made up of mathematical derivations and a Monte Carlo simulation to demonstrate the accuracy of his method.
- The British scientist and author Richard Dawkins called R.A. Fisher “a genius who almost single-handedly created the foundations for modern statistical science.” Some of Fisher’s most important work include his theories of experimental design, analysis of variance, and introducing the concept of making guesses of unknowns based on maximum likelihood (the concept of approximating an unknown value based on the number that makes related data most likely). On top of all this, he was a hugely influential biologist.