Andrew has a great post about menu engineering.
Designing restaurant menus has become a science, and the referenced BBC article offers up a parade of the peer-reviewed findings from that field.
Some headline grabbers include weight of menu is associated with upscale restaurants (kind of like weight of business cards), scripted writing is also deemed higher class, longer words are better, first items are ordered more, and words that "mimic mouth movements" related to the food are more popular.
This type of research has two ingredients that raise their viral potential: the researchers use "conventional wisdom" as foil, either legitimizing them through "science" ("I was right!") or casting doubt ("Everyone is wrong!"); the findings make for light, fun headlines.
Nevertheless, Andrew's post concerns the quality of the underlying research.
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Since I've been waxing about research methods of Covid-19 studies, let's also talk about the type of methodology typically deployed for food science studies.
The researchers recruit a bunch of people, usually a few hundred at the most, sometimes college students, and they put them in a "lab experiment" in which they are exposed to (say) menus that are heavier or lighter in weight. Because it's an lab experiment, the participants are randomly assigned to these treatment groups, and then they fill out a survey which measures outcomes.
This research method is very common in social science academia, but in recent years, people are discovering that they often fail to replicate. (Lots of posts about this scientific crisis on Andrew's blog.)
The lab experiment method has a number of problems. First, the lab scenario is completely contrived. The participants are not actually hungry, ordering food, or consuming them. They are not spending real money.
Second, the participants are not a representative sample of the population to which the research will be generalized. The recruits are not typical diners (and certainly, not customers of upscale restaurants).
Third, saying in a survey that they will pay more for something doesn't mean they will actually pay more real money for it.
Fourth, such research typically assumes no meaningful variation in human behavior. The sample size is too small to account for such variability. The investigators are looking for a universal behavior - and they include just enough samples to achieve a magical p-value threshold required for scientific publication.
Fifth, almost all such research isolate a single factor. It is a well-known fallacy in design of experiments to add the effects of separate one-factor studies - remember, each experiment assumes all else equal.
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I left the following comment:
Now, wake me up when they do an actual statistical experiment. They could partner with a restaurant chain, design an experiment involving all of the above factors, print different versions of the menu, and randomly assign them to real diners who make real orders. As a foodie, I’d love to be part of this research team!
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