The vast majority of statistical analysis is not performed by statisticians14 Jun 2013
Whether you know it or not, everything you do produces data - from the websites you read to the rate at which your heart beats. Until pretty recently, most of the data you produced wasn’t collected, it floated off unmeasured. The only data that were collected were painstakingly gathered by scientists one number at a time in small experiments with a few people. This laborious process meant that data were expensive and time-consuming to collect. Yet many of the most amazing scientific discoveries over the last two centuries were squeezed from just a few data points. But over the last two decades, the unit price of data has dramatically dropped. New technologies touching every aspect of our lives from our money, to our health, to our social interactions have made data collection cheap and easy (see e.g. Camp Williams).
To give you an idea of how steep the drop in the price of data has been, in 1967 Stanley Milgram did an experiment to determine the number of degrees of separation between two people in the U.S. In his experiment he sent 296 letters to people in Omaha, Nebraska and Wichita, Kansas. The goal was to get the letters to a specific person in Boston, Massachusetts. The trick was people had to send the letters to someone they knew, and they then sent it to someone they knew and so on. At the end of the experiment, only 64 letters made it to the individual in Boston. On average, the letters had gone through 6 people to get there. This is where the idea of “6-degrees of Kevin Bacon” comes from. Based on 64 data points. A 2007 study updated that number to “7 degrees of Kevin Bacon”. The study was based on 30 billion instant messaging conversations collected over the course of a month or two with the same amount of effort.
Once data started getting cheaper to collect, it got cheaper fast. Take another example, the human genome. The genome is the unique DNA code in every one of your cells. It consists of a set of 3 billion letters that is unique to you. By many measures, the race to be the first group to collect all 3 billion letters from a single person kicked off the data revolution in biology. The project was completed in 2000 after a decade of work and $3 billion to collect the 3 billion letters in the first human genome. This project was actually a stunning success, most people thought it would be much more expensive. But just over a decade later, new technology means that we can now collect all 3 billion letters from a person’s genome for about $10,000 in about a week.
As the price of data dropped so dramatically over the last two decades, the division of labor between analysts and everyone else became less and less clear. Data became so cheap that it couldn’t be confined to just a few highly trained people. So raw data started to trickle out in a number of different ways. It started with maps of temperatures across the U.S. in newspapers and quickly ramped up to information on how many friends you had on Facebook, the price of tickets on 50 airlines for the same flight, or measurements of your blood pressure, good cholesterol, and bad cholesterol at every doctor’s visit. Arguments about politics started focusing on the results of opinion polls and who was asking the questions. The doctor stopped telling you what to do and started presenting you with options and the risks that went along with each.
That is when statisticians stopped being the primary data analysts. At some point, the trickle of data about you, your friends, and the world started impacting every component of your life. Now almost every decision you make is based on data you have about the world around you. Let’s take something simple, like where are you going to eat tonight. You might just pick the nearest restaurant to your house. But you could also ask your friends on Facebook where you should eat, or read reviews on Yelp, or check out menus on the restaurants websites. All of these are pieces of data that are collected and presented for you to "analyze".
This revolution demands a new way of thinking about statistics. It has precipitated explosive growth in data visualization - the most accessible form of data analysis. It has encouraged explosive growth in MOOCs like the ones Roger, Brian and I taught. It has created open data initiatives in government. It has also encouraged more accessible data analysis platforms in the form of startups like StatWing that make it easier for non-statisticians to analyze data.
What does this mean for statistics as a discipline? Well it is great news in that we have a lot more people to train. It also really drives home the importance of statistical literacy. But it also means we need to adapt our thinking about what it means to teach and perform statistics. We need to focus increasingly on interpretation and critique and away from formulas and memorization (think English composition versus grammar). We also need to realize that the most impactful statistical methods will not be used by statisticians, which means we need more fool proofing, more time automating, and more time creating software. The potential payout is huge for realizing that the tide has turned and most people who analyze data aren't statisticians.