Skip to comments.You Too Can Be A Genius After 10,000 Hours
Posted on 05/17/2010 5:20:54 AM PDT by mattstat
No, you cannot. That title is a lie, and, judging by a recent spate of books on the subject, a popular one.
Ann Hulbert of Slate has compiled a list of books which preach the Gospel of Success (HT A&LD).
Gladwells Outliers: The Story of Success was not, appropriately enough, a bolt of original genius when it appeared in November 2008. Geoffrey Colvins Talent Is Overrated: What Really Separates World-Class Performers From Everybody Else had come out a month earlier. The following spring brought Daniel Coyles The Talent Code: Greatness Isnt Born. Its Grown. Heres How. This spring David Shenks The Genius in All of Us: Why Everything Youve Been Told About Genetics, Talent, and IQ Is Wrong has gotten several raves. Hot on its heels arrives Bounce: Mozart, Federer, Picasso, Beckham, and the Science of Success, by Matthew Syed, a former Olympic ping-pong player turned journalist.
Gladwell and his followers are rotten statisticians. They look upon their sample of the successful and say, Hark! These shiny examples have all worked hard; their dedicated efforts brought them to the top. So too can elbow grease alight you on the pinnacle.
Diligence is key! After reaching a certain level of practice, anybody can reach the height of their professions. Talent is a nicety, not a necessity.
These are obviously false, beliefs based on bad sampling. It is fine to catalog the habits of the successful, but it is a mistake to conclude that those habits are what are solely responsible for achievement. Why? Because this neglects the vastly largerand hiddenpool of people who have adopted the same habits but who were not successful.
Its true ...
(Excerpt) Read more at wmbriggs.com ...
Gladwell did claim in his book that they were unable to find examples of people who had practiced piano for 10,000 hours and remained mediocre.
It may be that it would be impossible to find someone who would stick with something for 10,000 hours if it became obvious after a 1,000 hours or so that they didn’t have any talent for it.
In other words, Gladwell et al. may be committing the classic error of confusing cause with effect?
If that's your point, it's an excellent one!
I would say that Gladwell’s point remains unproven. I don’t see a way you could ever prove it. Even if you somehow set up a study where you could get people to work on something for 10,000 hours, how would you prove that everyone was putting forth equal effort in those 10,000 hours?
Certainly, but I do think it has a certain intuitive plausibility.
>> I dont see a way you could ever prove it. <<
Well, I come from the school that says hypotheses are never "proven," only "falsified" or "not falsified."
But that methodological/semantic quibble aside, I don't think it would be too hard to think up a multiple regression model for testing the hypothesis, or maybe even a structural equations model. Econometricians deal with such problems all the time, whereby they must disentangle cause from effect.
But once a logically coherent model is specificed, the problem would then be to get enough survey data and enough DNA data -- plus enough computer power to analyze the huge amount of data required. Probably not possible now, but the day may arrive during the next ten or 15 years!
>> Even if you somehow set up a study where you could get people to work on something for 10,000 hours, how would you prove that everyone was putting forth equal effort in those 10,000 hours? <<
With a large enough sample, you'd probably just assume that the data would produce a sort of "average" between the underachievers and the overachievers. Then once you try fitting the data to your model, you observe the t-scores of the regression coefficents. And whenever you find that t > 2.0, you've probably identified a signficant variable.
In other words, the data would speak for themselves. If you've specified a good model, and if your data are numerous enough to average out the kind of error you mention, then either you will or you won't get statistically significant coefficients.
If the former, your hypothesis still stands. If the latter, you may either reject the hypthesis -- or you may try to specify the model and/or seek better data. It's simply the on-going process of any empirical science.
Again, econometricians deal with such matters routinely. And by the way, they call the attempt to disentangle cause and effect "the identification problem." For a humorous reference, see here: