This paper introduced linear discriminant analysis(LDA), a statistical technique that revolutionized classification in biology and beyond.

Fisher demonstrated how to use multiple measurements to distinguish between different species of iris flowers, laying the foundation for modern multivariate statistics.

His work showed that combining several characteristics could provide more accurate classification than relying on any single trait.

This paper not only solved a practical problem in botany but also opened up new avenues for statistical analysis across various fields.

Fisher's method became a cornerstone of pattern recognition and machine learning, influencing diverse areas from medical diagnostics to AI.

The iris dataset he used, now known as the "Fisher iris" or "Anderson iris" dataset, remains a popular example in data science education and research.

This paper is considered one of the foundational works in modern statistical hypothesis testing.

Key insights and influences:

These concepts have had a profound influence on modern statistical theory and practice, forming the basis of much of classical hypothesis testing used today in various fields of science and research.

Shannon, Claude Elwood. "A mathematical theory of communication." The Bell system technical journal 27.3 (1948): 379-423.

Shannon, Claude Elwood. "A mathematical theory of communication." The Bell system technical journal 27.3 (1948): 379-423.

Shannon, Claude Elwood. "A mathematical theory of communication." The Bell system technical journal 27.3 (1948): 379-423.

  "Application of the Logistic Function to Bio-Assays" (1944), Berkson Joseph

It gained further prominence in the 20th century through applications in various fields, including biology and bio-assay. Joseph Berkson's 1944 paper, 'Application of the Logistic Function to Bio-Assay,' was pivotal in popularizing its use for estimating drug potency.

Berkson argued that the logistic function was a more statistically manageable and theoretically sound alternative to the probit function, which assumed that individual susceptibilities to a drug follow a normal distribution.

The logistic function's ability to be easily linearized via the logit transformation simplifies parameter estimation, making it an attractive choice for analyzing dose-response data.

We discuss Ronald A. Fisher's paper "On the Mathematical Foundations of Theoretical Statistics"(1922), which profoundly shaped the field of statistics by establishing key concepts such as maximum likelihood estimation, which is crucial for parameter estimation in statistical models.