In 1974 Richard Feynman gave a memorable speech to the newbies at Caltech, establishing an interesting term called âCargo Cult Scienceâ. In a nutshell, it boils down to the finding that many scientists (if not whole disciplines), apparently âdo scienceâ, use all the correct scientific methods and tools without actually ever getting any real conclusions out of it. Despite the fact that his speech â and warnings â are from the 1970ies, not much has changed. In fact, itâs probably gone downhill since then, not the least because of the ever-increasing pressure of publishing more and more peer-reviewed articles.
One perfect example for âcargo cultâ is statistical analysis, in particular the cult around âstatistical significanceâ. As it goes, some methods of statistical analysis have become so widespread and commonplace that the reasons behind using the in the first place is no longer questioned. I spent hours explaining how statistical significance is not scientific significance. Much worse, in many cases these statistical analyses are just applied plain wrong or the wrong conclusions are drawn from them. Being a physicist myself, I was quite surprised to find this, mostly because these methods are rarely used in physics [1].
Since Iâve recently come across such types of âCargo Cult Scienceâ in Food Science and I think it´s worth highlighting a couple of the effects this has had on the research. Iâll highlight three symptoms. If you come across them, chance is that you are dealing with bad case of Cargo Cult Science:
Symptom 1: âEquipment relicsâ: in lab classes you quickly learn never to trust instruments completely. First, because any instrument will have some systematic errors. Second, because almost any instrument needs a proper calibration first. There is no shortcut or substitute for that. If you want results you can trust, itâs you who has to do it. Regularly. Enter unconditional faith in âlaboratory grade equipmentâ. Turns out that people like to believe that just because a piece of equipment is more expensive and bought in a serious-looking catalogue for lab stuff, its readings are always rights. Itâs not. When readings donât make sense, the first thing you need to do is question your own readings before questioning basic science.
Symptom 2: âBecause everyone does it like thatâ: While itâs good practice to have a look around and see what and how others have been doing their studies or how theyâve written stuff, it does not relieve you from thinking for yourself nor critically judging what others have been doing.
Symptom 3: âBecause it sounds more scientificâ: Thatâs one of my biggest pet peeves. I read tons of things on writing papers. The common denominators are that you shouldnât bore your reader to death, nor use fancy-pancy language youâd never use in any other context. Using tons of âwhereasâ or ânonethelessâ doesnât make it more scientific. Nor does the use of the passive voice or sentences spanning half a page. Yet, the editor of a food journal recently forced us to rewrite an entire paper into passive voice since it would, aparrently, make it âmore objectiveâ. It doesnât. When we write âwe measuredâ, itâs actually true. We did it, not someone else. Writing âit was measuredâ might create the illusion of objectivity, but if so, then itâs a false one. Even worse, it makes texts unbearingly boring to read.
So whatâs one to do when he/she comes across those symptoms? First, always think for yourself. Itâs scary to see so many people just following bad example without even considering it. Second, donât give in too lightly, argue for whatâs right and against whatâs plain wrong. Naturally, a student will follow what the boss wants and I wouldnât recommend going against that unless you want to have a decent degree. But that doesnât mean that you should do the same when you write your paper or do your science. Third, learn to live with it and respect others even when you are convinced they are wrong.
[1] They are used of course but only when there is a necessary or sufficient reason to do so.
