Most of the technology of “confirmatory” non-qualitative research in both the social and natural sciences is aimed at preventing discovery. When confirmatory research goes smoothly, everything comes out precisely as expected. Received theory is supported by one more example of its usefulness, and requires no change. As in everyday social life, confirmation is exactly the absence of insight. In science, as in life, dramatic new discoveries must almost by definition be accidental (“serendipitous”). Indeed, they occur only in consequence of some mistake.
Kirk, Jerome, and Miller, Marc L., Reliability and Validity in Qualitative Research (Qualitative Research Methods). Sage Publications, Inc, Thousand Oaks, CA, 1985.
Viva exploratory methods in science! Viva exploratory methods in testing! Viva testers who study philosophy and the social sciences!
(Thank you Michael Bolton for finding this quote.)
Jari Laakso says
Looks like other sciences are also making studies from the exact same ideas from where exploratory testing and for example agile development are coming from:
“the key to great success is working harder in short bursts of time” = sessions
“The best performers set goals for their practice sessions and required themselves to take breaks.” = sessions
Besides, Pareto was making up this stuff already in the 19th century!
The Serendipitous Tester: Thank you Michael. What a wonderful discovery!
I didn’t know Michael Bolton was into exploratory testing 🙂
Exploratory testing to Confirmative testing, is very much like, Agile development to Waterfall development.
Justin Hunter (@Hexawise) says
No need to post this for your blog’s general audience. This one is more for you James than your readership (particularly since it’s not directly on point). I thought you might find this interesting. He was one of the co-authors (with George Box and my dad) of Statistics for Experimenters that
I was listening to an old but superb Design of Experiments presentation by Stu Hunter (no relation) today who made a different, but tangentially-related point here:
His point was that even when you run the same real world experiment, you’ll often observe different results. He uses the example of signing his name twice. Real world factors conspire against the creation of two identical signatures.
The second presentation Stu Hunter did on Introduction to Design of Experiments (in which he describes how to learn as much as possible in as few tests as possible) is even more interesting. https://www.youtube.com/watch?v=hTviHGsl5ag&list=PL262F34E45AE46400
[James’ Reply: This is cool. Thanks.]