Ronald Fisher | EPFL Graph Search

Are you an EPFL student looking for a semester project?

Work with us on data science and visualisation projects, and deploy your project as an app on top of GraphSearch.

Sir Ronald Aylmer Fisher (17 February 1890 – 29 July 1962) was a British polymath who was active as a mathematician, statistician, biologist, geneticist, and academic. For his work in statistics, he has been described as "a genius who almost single-handedly created the foundations for modern statistical science" and "the single most important figure in 20th century statistics". In genetics, his work used mathematics to combine Mendelian genetics and natural selection; this contributed to the revival of Darwinism in the early 20th-century revision of the theory of evolution known as the modern synthesis. For his contributions to biology, Fisher has been called "the greatest of Darwin’s successors". From 1919, he worked at the Rothamsted Experimental Station for 14 years; there, he analysed its immense body of data from crop experiments since the 1840s, and developed the analysis of variance (ANOVA). He established his reputation there in the following years as a biostatistician. Together with J. B. S. Haldane and Sewall Wright, Fisher is known as one of the three principal founders of population genetics. He outlined Fisher's principle, the Fisherian runaway and sexy son hypothesis theories of sexual selection. His contributions to statistics include promoting the method of maximum likelihood and deriving the properties of maximum likelihood estimators, fiducial inference, the derivation of various sampling distributions, founding principles of the design of experiments, and much more. Fisher held strong views on race and eugenics, insisting on racial differences. Although he was clearly a eugenicist, there is some debate as to whether Fisher supported scientific racism (see ). He was the Galton Professor of Eugenics at University College London and editor of the Annals of Eugenics. Fisher was born in East Finchley in London, England, into a middle-class household; his father, George, was a successful partner in Robinson & Fisher, auctioneers and fine art dealers.

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related concepts (115)

Related courses (11)

MATH-240: Statistics

Ce cours donne une introduction au traitement mathématique de la théorie de l'inférence statistique en utilisant la notion de vraisemblance comme un thème central.

MATH-413: Statistics for data science

Statistics lies at the foundation of data science, providing a unifying theoretical and methodological backbone for the diverse tasks enountered in this emerging field. This course rigorously develops

EE-209: Elements of statistics for data science

Related lectures (70)

Population genetics

Population genetics is a subfield of genetics that deals with genetic differences within and among populations, and is a part of evolutionary biology. Studies in this branch of biology examine such phenomena as adaptation, speciation, and population structure. Population genetics was a vital ingredient in the emergence of the modern evolutionary synthesis. Its primary founders were Sewall Wright, J. B. S. Haldane and Ronald Fisher, who also laid the foundations for the related discipline of quantitative genetics.

Ronald Fisher

P-value

In null-hypothesis significance testing, the p-value is the probability of obtaining test results at least as extreme as the result actually observed, under the assumption that the null hypothesis is correct. A very small p-value means that such an extreme observed outcome would be very unlikely under the null hypothesis. Even though reporting p-values of statistical tests is common practice in academic publications of many quantitative fields, misinterpretation and misuse of p-values is widespread and has been a major topic in mathematics and metascience.

Covers the Bayes discriminant rule for allocating individuals to populations based on measurements and prior probabilities.

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Covers ANOVA method, focusing on partitioning total sum of squares into treatment and error components, mean square calculations, Fisher statistic, and F-distribution.