Revolutions in statistics for the last 50 years? What areas of statistics have been substantially revolutionised in the last 50 years? For example, about 40 years ago, Akaike with colleagues revolutionised the area of statistical model discrimination. About 10 years ago, Hyndman with colleagues revolutionised the area of exponential smoothing. About XX years ago, ... 
How do I possibly continue the list, with years and names please? By statistics I mean its all four types from Bartholomew's 1995 presidential address, Chambers's greater and lesser statistics together, as featuring in Hand's recent presidential address on 'Modern statistics' and so on - anything professionally relevant.
 A: John Tukey's truly strange idea: exploratory data analysis.
http://en.wikipedia.org/wiki/Exploratory_data_analysis
A: Generalized linear models due to the recently deceased John Nelder and Robert Wedderburn.
A: There was a great discussion on metaoptimize called "Most Influential Ideas 1995 - 2005" 
Which holds a great collection of ideas.
The one I mentioned there, and will repeat here, is the "revolution" in the concept of multiple comparisons, specifically the shift from using FWE to FDR methods, for testing very many hypothesis (like in micro array or fMRI and so on)
Here is one of the first articles that introduced this notion to the scientific community: Benjamini, Yoav; Hochberg, Yosef (1995). "Controlling the false discovery rate: a practical and powerful approach to multiple testing". Journal of the Royal Statistical Society
A: The creation of this site ;-)
A: *

*Revolution 1: S (ACM Software Systems Award)

*Revolution 2: R (Ross Ihaka (1998) on the history of R to that point)

A: Cox proportional hazards survival analysis:
http://en.wikipedia.org/wiki/Cox_proportional_hazards_model
A: The Box-Jenkins approach to time-series modelling: ARIMA models etc.
http://en.wikipedia.org/wiki/Box-Jenkins
A: Efron's work on the Bootstrap comes to mind.
A: The application of Bayesian statistics with Monte Carlo methods. 
A: Ensemble methods like boosting, bagging, ... etc are another potential candidate.
A: In 1960 most people doing statistics were calculating with a four-function manual calculator or a slide rule or by hand; mainframe computers were just beginning to run some programs in Algol and Fortran; graphical output devices were rare and crude.  Because of these limitations, Bayesian analysis was considered formidably difficult due to the calculations required.  Databases were managed on punch cards and computer tape drives limited to a few megabytes.  Statistical education focused initially on learning formulas for t-testing and ANOVA.  Statistical practice usually did not go beyond such routine hypothesis testing (although some brilliant minds had just begun to exploit computers for deeper analysis, as exemplified by Mosteller & Wallace's book on the Federalist papers, for instance).
I recounted this well-known history as a reminder that all of statistics has undergone a revolution due to the rise and spread of computing power during this last half century, a revolution that has made possible almost every other innovation in statistics during that time (with the notable exception of Tukey's pencil-and-paper EDA methods, as Thylacoleo has already observed).
