How to overcome preconceived ideas about social scientists?
I get patronised by the guy whose randomised experiment allows him to get away with a t-test. How to react when that happens without sounding too defensive?
I think this is the key aspect in your question. Once the "hardsplainer" starts patronizing you, you are on the defensive. And there is really no good way out of this situation once you react by starting to defend your methods.
Sometimes the hardsplainer will realize that his assumptions were erroneous and that you may indeed know more about stats than he.
Or he will in turn get defensive and start nitpicking your methods, probably getting in deeper and deeper water as he is discussing stuff he may not know much about. This is not a good conversation to have at social gatherings.
I'm afraid the second possibility will happen rather frequently, simply because people are not good at revising preconceived impressions.
So I'd recommend that you nip the problem in the bud, by not allowing your interlocutor to, in fact, preconceive the impression of "look, a social scientist, who probably doesn't know anything about statistics". Specifically, when you discuss your work, invest half a sentence to name-drop your analysis techniques.
I'm looking at how foo relates to bar. Because I only have observational data, not experiments, I use econometrical panel data models, and I find that...
If you hint that you use advanced models right before the hardsplainer can get the wrong impression that he can lecture you with fundamentals, he will be stopped cold. (Of course, you don't want to overdo it to come across as an arrogant know-it-all.)
Yes, it would be nice if this were unnecessary, because people didn't have the preconceived notion that social scientists are inept in terms of statistics. Unfortunately, this notion does have a basis in facts. I do statistics for psychologists, and I see that while they do get a solid grounding in statistics, they do frequently misapply models, or interpret them incorrectly, or don't understand why p-hacking is a Bad Thing. Then again, some hard scientists do suffer from the delusion that being an expert in some hard science means that they automatically also are experts in statistics.
There is an important aspect of this dilemma which I did not see stated in your question: why is the opinion of the "hard-splainer" important to you?
Depending on the answer to this question, there are a number of different approaches that you might take. Although I am in a "hard" field myself, I face similar dilemmas in my interdisciplinary work, as I find that some researchers often dismiss or misunderstand computer science as "just data analysis," since their own experience with it has been largely limited to simple uses of Matlab, Excel, or specialized data analysis programs.
I have found that it is useful to develop a spectrum of responses, depending on my degree of investment in the interaction. From least to most investment, these are approximately:
- Nod and smile. When dealing with a random boor in an airplane or at a conference cocktail hour, I may simply choose to not engage. Why should I care what a fool believes when they cannot even bother to draw breath long enough for me to speak? So I nod, smile, say something politely vague, and then go to refill my drink / take care of some work / whatever.
Turn the tables. If the person seems worth talking to and capable of listening, but is uninformed, then I'll turn it into a teaching moment:
"Ah, it sounds like you're suffering from some common misconceptions about [subject]..."
It's good to spend some time with philosophy of science and the history of your field, in order to understand why things are the way they are. I have some favorite examples and anecdotes, mostly having little to do with my own work, which help illustrate the base pop-science-level points I'd want to make. Don't be defensive: instead, have fun sharing your love of the field and its complexities, and your conversation partner is more likely to enjoy themselves too and actually learn something about your field.
- Face the problem head-on For people whose opinion really matters to me, such as potential collaborators, program managers, and decision-making committees, I often actually have pre-prepared slides or, in some cases, actual published papers. You don't have to listen to a lecture, but instead say something like, "It sounds like you're concerned about [issue]," and then head for your careful "101-level" explanation of how that issue is approached in your work. Preparing material of this type can actually be a surprisingly valuable exercise for yourself as well: I have found that many of the assumptions of my field, while valid, have a much more complex backstory than I commonly think about while working within those assumptions, and taking time to understand them has opened up new knowledge and opportunities for me.
Given the example you indicate, how about: "Yeah, a t-test is probably just fine if one has good data. But if you have really bad, realistic, data, you need much more sophisticated methods such as ..."
Ideally mention computational methods the techo (the "techno-macho", or hardsplainer in your language) is not likely to know or understand. If they had only sought to show off their superiority, that'll make them go away.
And if the person is seriously interested to learn, then, that's fine, too. In this case, go forth and explain.