# Famous female statisticians

*(Updated 24 April 2015)*

One of my day jobs is teaching psychology students how to do data analysis. Occasionally I quote famous statisticians, for instance to illustrate ways of thinking about analysis, the subjective nature of modeling data, and other fun things. I mention the likes of William Gosset (Guinness and t-tests), George Box (all models are wrong), and Bruno de Finetti (probabilities don’t exist).

Most—often all—of my students are women. Most of my current collection of quotations are from men. This is a problem. So, I’m currently looking for examples of famous female statisticians (broadly interpreted; including data scientists, economists, quantitative social scientists). Here’s my current list. Suggestions for others would be most welcome, especially if you have a quotation I can use (turns out that statisticians write in maths most of the the time so it can be hard to find nice quotes).

- Daphne Koller (Professor in Stanford University; wide range, e.g., conditional independence models, feature selection)
- Deirdre McCloskey (economist, writes on applied stats, e.g., regression; she is also transgender)
- Fiona Steele (Professor in Stats at LSE, e.g., multilevel modeling)
- Florence Nightingale (data visualisation and public health stats)
- Gertrude Mary Cox (experimental design and analysis of experiments)
- Helena Chmura Kraemer (Professor of Biostatistics in Psychiatry, Emerita, at Stanford)
- Hilary Mason (“enthusiastic member of the larger conspiracy to evolve the emerging discipline of data science”)
- Hilary Parker (data analyst at Etsy; PhD in biostatistics, genomics; useR)
- Irini Moustaki (Professor in Social Statistics at LSE)
- Jane Hillston (Professor of quantitative modelling at Edinburgh University; invented the stochastic process algebra, PEPA)
- Jennifer Neville (e.g., data mining for relational data such as networks/graphs)
- Juliet Popper Shaffer (work on corrections for multiple hypothesis testing)
- Pat Dugard (e.g., randomisation stats for single case and small-N multiple baseline studies)
- Rachel Schutt (Senior Vice President of Data Science at News Corp)
- Stella Cunliffe (worked in Guinness and first female president of RSS)
- Susan A. Murphy (e.g., clinical trial design; methods for multi-stage decision making)
- Victoria Stodden (e.g., reproducibility of models, codes)

**Quotations—work in progress**

“The newly mathematized statistics became a fetish in fields that wanted to be sciences. During the 1920s, when sociology was a young science, quantification was a way of claiming status, as it became also in economics, fresh from putting aside its old name of political economy, and in psychology, fresh from a separation from philosophy. In the 1920s and 1930s even the social anthropologists counted coconuts.”

—Deirdre McCloskey, The Trouble with Mathematics and Statistics in Economics

“The Cabinet Ministers, the army of their subordinates… have for the most part received a university education, but no education in statistical method. We legislate without knowing what we are doing. The War Office has some of the finest statistics in the world. What comes of them? Little or nothing. Why? Because the Heads do not know how to make anything of them. Our Indian statistics are really better than those of England. Of these no use is made in administration. What we want is not so much (or at least not at present) an accumulation of facts, as to teach men who are to govern the country the use of statistical facts.”

—Florence Nightingale in a letter to Benjamin Jowett, from Kopf, E. W. (1916). Florence Nightingale as statistician. *Publications of the American Statistical Association*, *15*(116), 388–404.

“To understand God’s thoughts we must study statistics, for these are the measure of His purpose.”

—Florence Nightingale

“The statistician who supposes that his main contribution to the planning of an experiment will involve statistical theory, finds repeatedly that he makes his most valuable contribution simply by persuading the investigator to explain why he wishes to do the experiment.”

—Gertrude M Cox

“It is no use, as statisticians, our being sniffy about the slapdash methods of many sociologists unless we are prepared to try to guide them into more scientifically acceptable thought. To do this, there must be interaction between them and us.”

—Stella V Cunliffe (1976, p. 9). Interaction. Journal of the Royal Statistical Society. Series A (General), 139, 1–19.

**Thanks…**

… to everyone who sent suggestions!

“Familiarity and comfort with statistics and probability help when evaluating scientific measurements…..I was reminded of the virtue of probabilistic reasoning when a friend was frustrated by my “I don’t know” response to his question about whether or not I planned to attend an event the following evening. Fortunately he was a gambler and mathematically inclined. So instead of insisting on a definite reply, he asked me the odds. To my surprise I found that question a lot simpler to deal with. Even thought the probability estimate I gave him was only a rough guess, it more closely reflected my competing considerations and uncertainties than a definite yes or no reply would have done”. Lisa Randall – Knocking on Heaven’s Door

I thought of your post while reading this. I figured you’d appreciate a humorous quote as well.