Over the years I have listened to younger people telling me they are not happy with their situation. I’m no counsellor, and have never claimed to be: I don’t like giving advice, and usually say so, and I’m reluctant to generalize from the n=1 case studies I know well (my own).
So what do I do? Usually, I just listen, though I will point out inconsistencies—if only to show that I’m listening. I try to avoid making judgments, and I rarely feel happy suggesting things for others to do. But I do ask questions, and that can be a give-away, partially revealing to what I think, and what I think someone should do. What have I learned? Sad to say, not much more that I could have learned with a few web searches, or from reading the writings of Lao Tzu (in a reliable translation). But I needed to know the keywords with which to search, or the aphorisms to note well, and they have taken me many years to learn.
Consider the term external validation. This is not an expression that rolls easily off my tongue, but it describes very well what I’ve often heard, so I’ve embraced it. I meet unhappy students (and others) who are uncertain whether their work is good enough, looking for praise, feeling deeply saddened by its absence—or worse, convinced that they are no good unless someone tells them they are. In many cases this praise comes from a high-achieving person, with lots of external evidence of their abilities. Perhaps I’m a bad boss in this respect, because I rarely take time off to praise, to congratulate, or to boost, thinking that there are usually better things to do with my time with others than back-patting. Also, I’ve always felt that an important part of becoming competent is learning how to assess one’s own work, so I think I unconsciously force this issue a little.
Another form of external validation is the need to be appreciated. In general, in our IMS community and elsewhere, contributions to theory are more highly valued than those to applications, so applied statisticians may feel unappreciated. We hear a lot about data science these days, and many of us feel that a good deal of the hype is what we have been doing for much of our lives: applied statistics. Clearly, the funding bodies, presidents and deans pouring money into data science don’t appreciate us. So what? Of course elsewhere probabilists are probably feeling unappreciated, perhaps by mathematicians. The web has a lot to offer on this, including 35 Quotes On How To Care Less About What Others Think, one of which is attributed (most likely falsely) to Lao Tzu: Care about what other people think and you will always be their prisoner.
Something I hear a lot from unhappy people is that everyone else is better than them. This can be crippling. I’ve seen it in students who join the Berkeley stat graduate program, and become surrounded by people who seem to be so much more capable, more productive, more promising than they are. This feeling of inferiority can kill their joy, extinguish their ambitions and make it hard or even impossible for them to continue. I try to point out that aptitude for statistics has many dimensions, and that even if it only had one dimension, there will always be people above you and people below you. Is it likely that the number one statistician is the only happy one? What does your position in the ranking matter if you are happy doing what you are doing? Again, the web has lots on this, including How to Stop Comparing Yourself to Others, and another aphorism attributed to Lao Tzu: When you are content to be simply yourself and don’t compare or compete, everyone will respect you.
For me as a listener, my challenge is to get people moving in a better direction without telling them what to do. Writing (on the web) in Psychology Today Elizabeth R Thornton calls the issues I have discussed mental models, and asks: “Do yours help or hurt you?”
Statisticians are very familiar with models, and know that some are fit for their purpose, while others are not. We have diagnostics to examine models, and ways of finding better models. It seems to me that when one of us is unhappy for reasons similar to those I have described above, we might draw on the model analogy. We could scrutinize our current mental models for deficiencies, and perhaps move to alternatives that might help rather than hurt us. As with the statistics literature, there is lots of advice on how to do this in books, articles and blogs.
Changing your mental models might be all it takes to become happier.
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