So I've been accepted to Hofstra law as well as California Western. California Western is going to give me a 45,000 scholarship for the three years that I attend. I have two questions:Veteran TLS commenters will be sure to inform this misguided soul that attending Hofstra at sticker or Cal Western with a $15K per year "scholarship" (really a cross-subsidized tuition discount) is a horrible idea under almost any possible circumstance. The original poster may well then resist this advice, by making some combination of the following claims:
1st- Would I be better off moving from California to New York to attend Hofstra (tier 2 school) or stay in California to attend Cal Western ( tier 4 school). Basically is it a smart idea to move for a tier 2 school.
2nd- If I do attend Hofstra can I ever make my way back to California. I hear that where ever you go to laws school most people usually stay in the area because of the networking and job prospects are usually better. I'm just wondering if I stay and work a couple of years and gain some experience in New York will I be able to use that to gain a job in California.
(1) I plan to work exceptionally hard in law school and finish in the top 10% of my class.
(2) After killing it in my first year I will transfer to a much better school.
(3) I have met several very successful lawyers who graduated from Hofstra/Cal Western.
(4) If I'm only making $50K a year as a lawyer after I graduate I can go into this government debt forgiveness program that I've heard about, and after all $50K is just a starting salary.
(5) There are a bunch of special circumstances about me that make my situation different from those of most people who have my entrance stats.
In other words, statistical extrapolation doesn't really apply in my case, because I'm not a statistic.
It's very tempting to interpret this sort of response moralistically as a sign of a flawed personal character, or to pathologize it as a symptom of a psychological deficit (in fact the latter is what the phrase Special Snowflake Syndrome does), or to simply treat it as evidence of stupidity. But such responses gloss over the extent to which the sheer ubiquity of SSS indicates that it is really a product of deep structural factors more than of individual moral weaknesses or cognitive deficits.
In fact, Special Snowflake Syndrome could be re-characterized as "Thinking Like a Properly Socialized American" (or at least a properly socialized American from those social classes that produce the vast majority of law students).
Consider three central features of SSS: optimism bias, confirmation bias, and causal bias.
Optimism bias: Americans in general, and middle and upper middle class Americans in particular, are socialized to be optimistic, in the sense that they are encouraged to believe that the chances of a good outcome for them personally are higher than average, and, even more powerfully, that the chances of a bad outcome for them are lower than average. Of course this is a nonsensical belief from a statistical standpoint, but perhaps the most important element of SSS is that Americans are also socialized not to believe in the predictive value of statistics as applied to themselves as individuals. (This belief, by the way, turns out to be perfectly compatible with a strong belief in the predictive value of statistics as applied to others.)
Confirmation bias: People have a strong cognitive bias toward paying attention to information they find pleasing, while ignoring data they find disturbing. This again is a manifestation of how difficult it is for us to genuinely embrace statistical modes of reasoning, at least in regard to ourselves, and subjects we care deeply about. Anecdotes that confirm our biases are interpreted as presumptively meaningful; carefully controlled studies challenging those biases are flawed, cherry-picked, and examples of how you can make statistics say anything.
Causal bias: One reason people hate statistical reasoning is that such reasoning requires embracing the large role that random factors play in outcomes. Our minds hate randomness, because randomness is something we can't control. Here's an example, provided by the statisticians Howard Wainer and Harris Zwerling, and referenced in Daniel Kahneman's excellent book, Thinking, Fast and Slow: A study of the incidence of kidney cancer in the 3,141 counties in the USA reveals a striking pattern. The counties in which the incidence is lowest are mostly rural, sparsely populated, and located in traditionally Republican states in the South, the Midwest, and the West. What explains this pattern?
It's easy enough to come up with all sorts of plausible-sounding theories for why this might be the case (lower pollution levels, healthier food, higher activity levels, etc.) Well it turns out that the counties which have the highest incidence of kidney cancer are . . . mostly rural, sparsely populated, and located in traditionally Republican states in the South, Midwest, and West. Again it's easy to come up with theories as to why; rural poverty, no access to good health care, high-fat diets, higher tobacco use, etc.
Now obviously these various theories completely contradict each other, and the real explanation is a statistical artifact: counties with low populations are as a consequence of their low populations much more likely to produce statistically outlying results. In other words, the correlations observed have no causal significance at all. But we resist this explanation. We like causal reasoning, because causal reasoning produces a sense of control ("don't live in a polluted area; don't eat high-fat food"), while embracing randomness leads to the opposite sensation ("don't be unlucky enough to contract kidney cancer.").
Special Snowflake Syndrome is just an extension of these strongly socialized cognitive biases. Blaming people for failing to take "personal responsibility" for their "bad choices" is akin to blaming them for being members of this culture. All of which is to say that a structural problem requires a structural response, rather than exhorting individuals to become different people.