In one of the lab courses I took as an undergraduate, I remember that the professor noted while discussing some statistical test (almost certainly chi-squared) that one could use it to show that a lot of early scientists (prior to the development of rigorous statistical analysis) had data that were too good to be realistic, and thus (with very high probability) were either deliberately falsified or the result of poor experimental design. He in particular cited Mendel as an example of a scientist whose data were far too good to be believable.
Wikipedia has a section related to Mendel's case specifically, and some discussion as to the possibilities. I'm more interested in the general case.
Is it true that a large fraction (relative to today) of prominent scientists before the 20th century presented data to support their conclusions which were statistically too good to be true? And if so, how did they avoid being wrong about a lot of their results?