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When I was a teenager I broke all the rules on Friday night. After dinner I would watch Louis Rukeyser’s Wall Street Week at 8:30pm, and I would be in bed an hour later. On new year’s eve, he had a special “year-end review”, during which he hosted “financial experts” who would opine on the stock market and make predictions for the coming year.
What I learned from Louis Rukeyser was:
1. Never trust men in suits (or tuxedos).
2. It’s easier to perpetrate the 1024 scam than one might think!
Here are the experts in 1999 all predicting increases for the stock market in 2000:
As it turned out, the NASDAQ peaked on March 10, 2000, and within a week and a half had dropped 10%. By the end of the year the dot-com bubble had completely burst and a few years later the market had lost almost 80% of its value.
Predictions on the last day of the 20th century represented a spectacular failure for the “pundits”, but by then I had already witnessed many failures on the show. I’d also noted that almost all the invited “experts” were men. Of course correlation does not imply causation, but I remember having a hard time dispelling the notion that the guests were wrong because they were men. I never wanted to be sexist, but Louis Rukeyser made it very difficult for me!
Gender issues aside, the main lesson I learned from Louis Rukeyser’s show is that it’s easy to perpetrate the 1024 scam. The scam goes something like this: a scammer sends out 1024 emails to individuals that are unlikely to know each other, with each email making a prediction about the performance of the stock market in the coming week. For half the people (512), she predicts the stock market will go up, and for the other half, that it will go down. The next week, she has obviously sent a correct prediction of the market to half the people (this assumes the market is never unchanged after a week). She ignores the 512 people who have received an incorrect prediction, dividing those who received the correct prediction into two halves (256 each). Again, she predicts the performance of the market in the coming week, sending 256 individuals a prediction that the market will go up, and the other 256 a prediction that it will go down. She continues this divide-and-conquer for 10 weeks, at which time there is one individual that has received correct predictions about the movement of the stock market for 2.5 months! This person may believe that the scammer has the ability to predict the market; after all, which looks like a very significant p-value. This is when the scammer asks for a “large investment”. Of course what is missing is knowledge of the other prediction emails sent out, or in other words the multiple testing problem.
The Wall Street Week guest panels essentially provided a perfect setting in which to perpetrate this scam. “Experts” that would err would be unlikely to be invited back. Whereas regular winners would be back for another chance at guessing. This is a situation very similar to the mutual fund management market, where managers are sacked when they have a bad year, only to have large firms with hundreds of funds on the books highlight funds that have performed well for 10 years in a row in their annual glossy brochures. But that is not the subject matter of this blog post. Rather, it’s the blog itself.
I wrote and posted my first blog entry (Genesis of *Seq) exactly a year ago. I began writing it for two reasons. First, I thought it could be a convenient and useful forum for discussion of technical developments in computational biology. I was motivated partly by the seqanswers website, which allows users to share information and experience in dealing with high-throughput sequence data. But I was also inspired by the What’s New Blog that has created numerous bridges in the mathematics community via highly technical yet accessible posts that have democratized mathematics. Second, I had noticed an extraordinary abuse of multiple testing in computational biology, and I was desperate for a forum where I could bring the issue to peoples attention. My initial frustration with outlandish claims in papers based on weak statistics had also grown over time to encompass a general concern for lack of rigor in computational biology papers. None of us are perfect but there is a wide gap between perfect and wrong. Computational biology is a field that is now an amalgamation of many subjects and I hoped that a blog would be able to reach the different silos more effectively than publications.
And thus this blog was born on August 19th 2013. I started without a preconception of how it would turn out over time, and I’m happy to say I’ve been surprised by its impact, most notably on myself. I’ve learned an enormous amount from reader feedback, in part via comments on individual posts, but also from private emails to me and in personal conversations. For this (selfish) reason alone, I will keep blogging. I have also been asked by many of you to keep posting, and I’m listening. When I have nothing left to say, I promise I will quit. But for now I have a backlog of posts, and after a break this summer, I am ready to return to the keyboard. Besides, since starting to blog I still haven’t been to Las Vegas.