Yes, I read the article and I applied an idea I took from one of the first articles on Nate Silver's 538 'news' site [0] of using Bayesian statistics to decode news as you read it and in my opinion the data presented is very underwhelming and there isn't any firm ground they are standing on here.
Here's my 'Bayesian logic' applied to it. My 'prior believe' in the claim being made is that I don't believe Uber has broad penetration across all demographics. Yes, people who are in the tech world and under 30 know of them, but not much outside of that. I was the first one of all my friends to use Uber (I would know because I have gotten over $60 in credits for signing them up). So outside of SF, my initial personal belief is that Uber doesn't have wide usage. So either the largest demographic of DUI violators are nerds who are younger than 30 or Uber is magically being used by people who would prefer using a more expensive Uber service than driver over a cheap taxi ride.
So let's look at the evidence they provide to see if it changes my prior significantly: one city was explicitly given data about without any hint as to what time-frame they are talking about. The other cities were literally the equivalent of hearsay arguments so because I'm the judge of this court, that evidence doesn't count. When you're talking about month-over-month and even year-over-year trends, a drop of 10% is just as easily explained by a random process modeled as a poisson [1] over the entrance of Uber. I would know having dealt with lots of time series data.
One finally comment, my apologies to the author of the bar chart, but that is just terrible. It's hard to read and doesn't make much sense from looking at it first. How many bar-chart graphs are out there that look like that? It could just be me speaking aesthetically because I'm weirded out by the random floating bars in the middle, but I feel Edward Tufte would have something to say about that.
jusitizin, I know you weren't speaking to me directly, but I read the article. Hope this comment proves it :)
Here's my 'Bayesian logic' applied to it. My 'prior believe' in the claim being made is that I don't believe Uber has broad penetration across all demographics. Yes, people who are in the tech world and under 30 know of them, but not much outside of that. I was the first one of all my friends to use Uber (I would know because I have gotten over $60 in credits for signing them up). So outside of SF, my initial personal belief is that Uber doesn't have wide usage. So either the largest demographic of DUI violators are nerds who are younger than 30 or Uber is magically being used by people who would prefer using a more expensive Uber service than driver over a cheap taxi ride.
So let's look at the evidence they provide to see if it changes my prior significantly: one city was explicitly given data about without any hint as to what time-frame they are talking about. The other cities were literally the equivalent of hearsay arguments so because I'm the judge of this court, that evidence doesn't count. When you're talking about month-over-month and even year-over-year trends, a drop of 10% is just as easily explained by a random process modeled as a poisson [1] over the entrance of Uber. I would know having dealt with lots of time series data.
One finally comment, my apologies to the author of the bar chart, but that is just terrible. It's hard to read and doesn't make much sense from looking at it first. How many bar-chart graphs are out there that look like that? It could just be me speaking aesthetically because I'm weirded out by the random floating bars in the middle, but I feel Edward Tufte would have something to say about that.
jusitizin, I know you weren't speaking to me directly, but I read the article. Hope this comment proves it :)
[0]
http://fivethirtyeight.com/features/a-formula-for-decoding-h...
[1]
http://www.wired.com/2012/12/what-does-randomness-look-like/