Why quantify?

From InterSciWiki

John McGraw asked in week 8: does complexity, study of complex systems, require quantification?

best answer that of Larry Wasserman in his web page for his 2004 book (All of Statistics) http://www.stat.cmu.edu/~larry/all-of-statistics/ (it also contains files with R code which students can use for doing all the computing). Given his outline summary, his section II summary is relevant:

I Probability theory, the formal language of uncertainty, and basis for statistical inference

II Statistical inference, the inverse of probability: Given the outcomes, what can we say about the process that generated the data?

III Applications: regression, graphical models, causation, density estimation, smoothing, classification, and simulation.

Given the outcomes (observed data): what can we say about the process that generated the data?

Take home: using probability theory, and then its inverse, statistical inference, this question can be answered. That was the theme of Cosma Shalizi's talk.

for more: Quantitative history

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