Just fwiw. My background (for 17 years anyway) was statistics in a human performance research environment. I've read Gladwell's book and although I have a few issues with his work, overall I found it a fun read. He is using the term "outliers" the way we used it in statistics. There is no connotation of "good" or "bad". Those are labels that depend on some other value judgement or set of criteria. An outlier is simply some datapoint that falls well outside the predicted/expected results. That guy who screws up the grading curve in class. It isn't an issue of "good" or "bad", simply being far outside predicted/expected range. They are a source of constant pain and frustration for statisticians because they can wreak havoc with correlations and studies due to their extremity. But you run great risk in ignoring outliers as the existence of outlying data is often a signal of another unidentified factor.
Just fwiw. "Outlier" does not imply any sort of value judgement. It is just an expression of a piece of data that doesn't "fit".
Your post leads to an observation and a question.
First, the observation. Ellis never used the term Outliers
in his book. He used it in a thread somewhere else in Aikiweb. I had not really thought much about the 'mechanics' of genius, but I read the book and realized that the 10,000 hours factor was crucial to aikido.
The strictly statistical use of outliers
does not really work in aikido, because there is no objective basis on which to ground the statistical aberration. I do not see how you can talk of outliers in aikido in the absence of clear statistical data about how the 'inliers' actually train.
Thus I am inclined to think that the use of the term in relation to Takeda and Ueshiba is not--cannot be--statistically based.
Secondly, the question. I mentioned in the TIE column that I believed Gladwell had been uncritical about the research of Geert Hofstede. However, I would be interested to hear more about your own reservations about Gladwell's research or putative results.