Oh there are many examples of this throughout science.

In my own area (machine learning), a decade ago there used to be a huge clique of researchers who's "consensus" was that ANNs were dead, SVM+kernel methods were superior, and that few other ML techniques mattered. Actually, the problem was simply that they were training ANNs improperly. Later researchers showed how to properly train ANNs, and the work of the Toronto machine intelligence group especially established that ANNs were quite superior to SVMs for many tasks.

In econometrics, subsequence time series (STS) clustering was widely thought to be a good approach for analyzing market movements. After decades of work and hundreds of papers on this technique, Keogh et al showed in 2005 that the results of STS clustering are actually indistinguishable from noise!

Another one, in physics, was pointed out by Lee Smolin in his book, The Trouble with Physics. In string theory it was commonly, but wrongly, consensus opinion that Mandelstam had proven string theory finite. Actually, he had only eliminated some particular forms of infinities. The work on establishing string theory as finite is still ongoing.