Some statistical methods - I do not remember if it is principal component analysis or something like that - are sometimes called "French data analysis". What is it exactly ? And some people say that this name is ironic, is it true, and why ?
French style data analysis is usually identified as work based on Correspondence Analysis and other spectrally-oriented work, but is actually more deeply grounded. Tim's reference to the Holmes piece is particularly helpful here.
A slightly broad picture would be to say that French style takes an axiomatic, geometrical, and mathematical approach to data matrices rather than a statistical modelling one. The term must be a little ironic because although CA was popularised by Benzecri, LeBart etc. (French) it has precursors in Hirschfeld (German) and successors in de Leeuw / Gifi (Dutch) and popularisers in Greenacre (South African). Greenacre also noted an important connection to generalised SVD and generated for me the only easily readable book on the topic. Discussions can get caustic -- see de Leeuw's review of Murtagh.
A useful example for seeing the comparing consequences of the style is in the analysis of crosstabulations. With a simple crosstab one might compare the 'French' style of simple Correspondence Analysis based on spectral decomposition of a suitably transformed table, with Association modeling (e.g. by Goodman, Clogg, or Haberman) based on structured interaction terms in an underlying log linear model. In fact these two approaches generate very similar parameterisations (and parameters!), but the focus is quite different. Agresti (1990) has a excellent discussion.
Maybe "correspondence analysis"? : http://en.wikipedia.org/wiki/Correspondence_analysis because it was primarily developed by a French researcher Jean-Paul Benzecri ?