Guinness, Gosset, Fisher, and Small Samples by Joan Fisher Box
Source: Statist. Sci. Volume 2, Number 1 (1987), 45-52.

No time for reading the whole article? I hope you have a few minutes to read following quotes, which are quite enchanting to me.

[p.45] One of the first things you learn in statistics is to distinguish between the true parameter value of the standard deviation σ and the sample standard deviation s. But at the turn of the century statisticians did not. They called both σ and s the standard deviation. They always used such large samples that their estimate really did approximate the parameter value, so it did not make much difference to their results. But their methods would not do for experimental work. You cannot get samples of thousands of experimental points. …

[p.49] …, the main question was exactly how much wider should the error limits be to make allowance for the error introduced by using the estimates m and s instead of the parameters μ and σ. Pearson could not answer that question for Gosset in 1905, nor the one that followed, which was: what level of probability should be called significant?

[p.49] …, Gosset worked out the exact answer to his question about the probable error of the mean and tabulated the probability values of his criterion z=(m-μ)/s for samples of N=2,3,…,10. He tried also to calculate the distribution of the correlation coefficient by the same method but managed to get the answer only for the case when the true correlation is zero. …

Quintessential Contributions:
Celebrating Major Birthdays of Statistical Ideas and Their Inventors
When: Saturday, September 27, 2008, 9:45 AM – 5:00 PM
Where: Radcliffe Gymnasium, 18 Mason Street, Cambridge, MA

This symposium features four distinguished speakers who will talk about four most celebrated statistical researches of four most renown statisticians. Click here for the details.

The contents are only spanned about 100 years and there are great chances that my mind still favors the history of astronomy over the history of statistics. However, there will be another presentation by Prof. Stigler on Monday (Sept. 29th) at the statistics department (click here for a pdf flyer) titled The Five Most Consequential Ideas in the History of Statistics and the last sentence “And, no, Bayes Theorem is not in the list.” in the abstract intrigues and tempts me to change my mind.

I’d like to share the information of this highly anticipated symposium and colloquium with you particularly with those who live in/near Cambridge.

• [astro-ph:0806.0650] Kimball and Ivezi\’c
  A Unified Catalog of Radio Objects Detected by NVSS, FIRST, WENSS, GB6, and SDSS (The catalog is available HERE. I’m always fascinated with the possibilities in catalog data sets which machine learning and statistics can explore. And I do hope that the measurement error columns get recognition from non astronomers.)

• [astro-ph:0806.0820] Landau and Simeone
  A statistical analysis of the data of Delta \alpha/ alpha from quasar absorption systems (It discusses Student t-tests from which confidence intervals for unknown variances and sample size based on Type I and II errors are obtained.)

• [stat.ML:0806.0729] R. Girard
  High dimensional gaussian classification (Model based – gaussian mixture approach – classification, although it is often mentioned as clustering in astronomy, on multi- dimensional data is very popular in astronomy)

• [astro-ph:0806.0520] Vio and Andreani
  A Statistical Analysis of the “Internal Linear Combination” Method in Problems of Signal Separation as in CMB Observations (Independent component analysis, ICA is discussed)

• [astro-ph:0806.0560] Nobel and Nowak
  Beyond XSPEC: Towards Highly Configurable Analysis (The flow of spectral analysis with XSPEC and Sherpa has not been accepted smoothly; instead, it has been a personal struggle. It seems the paper considers XSPEC as a black box, which I completely agree with. The main objective of the paper is comparing XSPEC and ISIS)

• [astro-ph:0806.0113] Casandjian and Grenier
  A revised catalogue of EGRET gamma-ray sources (The maximum likelihood detection method, which I never heard from statistical literature, is utilized)