When data is easy to collect, someone will ask you to collect it and someone else will query the data and compile a report with <i>percentages</i> in it. Then someone else will worry about some of the <i>percentages</i> being less or more than some <i>benchmark</i>. Then your work life will become less happy.<p>Example 1: Some years ago, I had to sit through a meeting where a committee worried about a <i>2% drop</i> in satisfaction scores on a student questionnaire. No-one checked how many replies were involved (around 400, so it worked out to about 6 people less in the second year than the first as the ratings were something like 75%).<p>Example 2: I recently had to add comments in a record system about students whose attendance <i>percentage</i> had dropped below 90%. That was 8 weeks into the course...
Sometimes I get the feeling that when we had less data, we were forced to think harder and more daringly.
I feel we lack new groundbreaking theoretical framework because of this.<p>I don't know if Newton's law's would jump out of the paper if you simply threw a ball at one million different vectors.
This is pretty much the same conclusion Ilya Grigorik (founder of postrank, which was recently bought by Google) came to: <a href="http://vimeo.com/22513786" rel="nofollow">http://vimeo.com/22513786</a>
Looking at the video, you could interpret his statement two ways. Either, the headline - <i>“When you have enough data, sometimes, you don’t have to be too clever”</i> OR the sort-of-opposite - <i>"AI has made so little progress that we don't anything much better than naive Bayes"</i>