I have the opposite problem to this question. Often when I talk to someone from a 'hard' scientific field I am 'taught' how statistics work or how programming works. It is a bit like being mansplained but by a hard scientist (hardsplaining, would that be a word?). Once someone was surprised that I know what the Runge-Kutta method is, and I have lost the count of how many times I have been lectured about stuff like the normal distribution and inferential statistics.
Now, I work with quantitative social science, using statistical methods that many would consider quite advanced (e.g. exponential random graph models for social networks, panel econometrics, simultaneous equations) and had the necessary mathematical training for that.
I am also very familiar with several programming languages. I usually write a lot of code in R or Python, besides being proficient in statistical scripting languages like Stata. I also know a good deal of NetLogo and Java, which I used when I did agent-based modelling. I would write my stuff in Sweave and LaTex if most journals didn't ask for a word document.
My understanding is that precisely because of the observational nature of most social science data, those of us who are into quantitative methods are forced to learn very advanced techniques to deal with issues such as selection bias and unobserved heterogeneity. Furthermore, the intrinsic 'messiness' of social data means that we have to be quite good at data management, usually learning a programming language or two in order to clean our datasets. Moreover, the emergent nature of social phenomena has motivated many of us to use multi-agent simulations in our work, demanding us to learn how to program.
Yet, I get patronised by the person whose randomised experiment allow them to get away with a t-test. How to react when that happens without sounding too defensive?
I know that generations of armchair sociologists theorising about the social construction of this and that probably created this stereotype of the mathematically inept computationally illiterate social scientist, but I believe that this image doesn't reflect a great share of those in my field.
EDIT 1: Given the close vote, I'm adding this clarification to what I'm asking. I want to know of strategies, possibly by others in a similar situation, to assert their research credentials or skills in a friendly manner in a social situation where the phenomenon described happens. The type of prejudice that I describe sometimes leads my opinion or ideas to be disregarded because of judgements made based on an incomplete image of my field of study. I believe that there are others there in a similar situation (Economists, for example), who may have strategies to cope with it. There must be a nice way to convey one's competence past the initial impression caused by the stereotype.