02-08-2021, 12:14 AM
Unfortunately, popular media often portray negative results as failures. If one searches Loch Ness thoroughly for a monster, and finds neither a monster nor an ecological niche for it to occupy, the result is wrongly reported as "inconclusive." (This sort of commitment to preformed conclusions may also lie behind a familiar incantation by the logically challenged: "you can't prove a negative.")
On the value of well-formed questions in computational research, it would be difficult to improve on D'Imperio's comment:
"One of the most demanding aspects of scientific work is the framing of useful questions, and the design of experiments that will produce useful answers. We need to apply this scientific approach to our study of the manuscript, and especially in our use of computers. In hand studies the limitations of patience and time on the part of the investigator effectively preclude many of the more wasteful activities, or at least prevent their assuming wasteful proportions, but the computer permits us to transcend these limitations and, alas, to carry out wasteful activities on a grand scale." (ouch)
That said, grandiose Big Data genomicists (whose subject can resemble ours) claim to practice "hypothesis-free" or "discovery" science. Are these just vogue descriptions of conventional and/or wasteful methods, or something new? Are they immune to negative outcomes?
On the value of well-formed questions in computational research, it would be difficult to improve on D'Imperio's comment:
"One of the most demanding aspects of scientific work is the framing of useful questions, and the design of experiments that will produce useful answers. We need to apply this scientific approach to our study of the manuscript, and especially in our use of computers. In hand studies the limitations of patience and time on the part of the investigator effectively preclude many of the more wasteful activities, or at least prevent their assuming wasteful proportions, but the computer permits us to transcend these limitations and, alas, to carry out wasteful activities on a grand scale." (ouch)
That said, grandiose Big Data genomicists (whose subject can resemble ours) claim to practice "hypothesis-free" or "discovery" science. Are these just vogue descriptions of conventional and/or wasteful methods, or something new? Are they immune to negative outcomes?