Logo Contest Winner

Congratulations to Bradley Saul, the winner of the Simply Statistics Logo contest! We had some great entries which made it difficult to choose between them. You can see the new logo to the right of our home page or the full sized version here:

I made some slight modifications to Bradley’s original code (apologies!). The code for his original version is here:

Here’s the code:
#########################################################
#  Project: Simply Statistics Logo Design
#  Date: 10/17/12
#  Version: 0.00001
#  Author: Bradley Saul
#  Built in R Version: 2.15.0
#########################################################

#Set Graphical parameters
par(mar=c(0, 0, 0, 0), pty='s', cex=3.5, pin=c(6,6))
#Note: I had to hard code the size, so that the text would scale
#on resizing the image. Maybe there is another way to get around font
#scaling issues - I couldn't figure it out.

make_logo <- function(color){
  
  x1 <- seq(0,1,.001)
  ncps <- seq(0,10,1)
  shapes <- seq(5,15,1)
  # Plot Beta distributions to make purty lines.
  plot(x1, pbeta(x1, shape1=10, shape2=.1, ncp=0), type='l', xlab='', ylab='', 
       frame.plot=FALSE, axes=FALSE)
  for(i in 1:length(ncps)){
    lines(x1, pbeta(x1,shape1=.1, shape2=10, ncp=ncps[i]), col=color)
  }

  #Shade in area under curve.
  coord.x <- c(0,x1,1)
  coord.y <- c(0,pbeta(x1,shape1=.1,shape2=10, ncp=10),0)
  polygon(coord.x, coord.y, col=color, border="white")

  #Lazy way to get area between curves shaded, rather than just area under curve.
  coord.y2 <- c(0,pbeta(x1,shape1=10,shape2=.1, ncp=0),0)
  polygon(coord.x, coord.y2, col="white", border="white")

  #Add text
  text(.98,.4,'Simply', col="white", adj=1,family='HersheySerif')
  text(.98,.25,'St*atistics', col="white", adj=1, family="HersheySerif")
}

Thanks to Bradley for the great logo and congratulations!

Reproducible Research: With Us or Against Us?

Last night this article by Chris Drummond of the Canadian National Research Council (Conseil national de recherches Canada) popped up in my Google Scholar alert. The title of the article, “Reproducible Research: a Dissenting Opinion” would seem to indicate that he disagrees with much that has been circulating out there about reproducible research.

Drummond singles out the Declaration published by a Yale Law School Roundtable on Data and Code Sharing (I was not part of the roundtable) as an example of the main arguments in favor of reproducibility and has four main objections. What I found interesting about his piece is that I think I more or less agree with all his objections and yet draw the exact opposite conclusion from him. In his abstract, he concludes that “I would also contend that the effort necessary to meet the [reproducible research] movement’s aims, and the general attitude it engenders, would not serve any of the research disciplines well.”

Interview with Tom Louis - New Chief Scientist at the Census Bureau

TL: Last winter, then-director Robert Groves (now Provost at Georgetown University) asked if I would be interested in  the possibility of becoming the next Associate Director of Research and Methodology (R&M) and Chief Scientist, succeeding  Rod Little (Professor of Biostatistics at the University of Michigan) in these roles.  I expressed interest and after several discussions with Bob and Rod, decided that if offered, I would accept.  It was offered and I did accept.  

As background,  components of my research, especially Bayesian methods, is Census-relevant.  Furthermore, during my time as a member of the National Academies Committee on National Statistics I served on the panel that recommended improvements in small area income and poverty estimates, chaired the panel that evaluated methods for allocating federal and state program funds by formula, and chaired a workshop on facilitating innovation in the Federal statistical system.

Rod and I noted that it’s interesting and possibly not coincidental that with my appointment the first two associate directors are both former chairs of Biostatistics departments.  It is the case that R&D’s mission is quite similar to that of a Biostatistics department; methods and collaborative research, consultation and education.  And, there are many statisticians at the Census Bureau who are not in the R&D directorship, a sociology quite similar to that in a School of Public Health or a Medical campus. 

TL: I became energized by the opportunity for national service, and excited by the scientific, administrative, and sociological responsibilities and challenges.  I’ll be engaged in hiring and staff development, and increasing the visibility of the bureau’s pre- and post-doctoral programs.  The position will provide the impetus to take a deep dive into finite-population statistical approaches, and contribute to the evolving understanding of the strengths and weakness of design-based, model-based and hybrid approaches to inference.  That I could remain a Hopkins employee by working via an Interagency Personnel Agreement, sealed the deal.  I will start in January 2013 and serve through 2015, and will continue to participate in some Hopkins-based activities.

In addition to activities within the Census Bureau, I’ll be increasing connections among statisticians in other federal statistical agencies, have a role in relations with researchers funded through the NSF to conduct census-related research.

Some academic thoughts on the poll aggregators

The night of the presidential elections I wrote a post celebrating the victory of data over punditry. I was motivated by the personal attacks made against Nate Silver by pundits that do not understand Statistics. The post generated a little bit of (justified) nerdrage (see comment section). So here I clarify a couple of things not as a member of Nate Silver’s fan club (my mancrush started with PECOTA not fivethirtyeight) but as an applied statistician.

The main reason fivethrityeight predicts election results so well is mainly due to the idea of averaging polls. This idea was around way before fivethirtyeight started. In fact, it’s a version of meta-analysis which has been around for hundreds of years and is commonly used to improve results of clinical trials. This election cycle several groups,  including Sam Wang (Princeton Election Consortium), Simon Jackman (pollster), and Drew Linzer (VOTAMATIC), predicted the election perfectly using this trick. 

While each group adds their own set of bells and whistles, most of the gains come from the aggregation of polls and understanding the concept of a standard error. Note that while each individual poll may be a bit biased, historical data shows that these biases average out to 0. So by taking the average you obtain a close to unbiased estimate. Because there are so many pollsters, each one conducting several polls, you can also estimate the standard error of your estimate pretty well (empirically rather than theoretically).  I include a plot below that provides evidence that bias is not an issue and that standard errors are well estimated. The dash line is at +/- 2 standard erros based on the average (across all states) standard error reported by fivethirtyeight. Note that the variability is smaller for the battleground states where more polls were conducted (this is consistent with state-specific standard error reported by fivethirtyeight).

Finally, there is the issue of the use of the word “probability”. Obviously one can correctly state that there is a 90% chance of observing event A and then have it not happen: Romney could have won and the aggregators still been “right”. Also frequentists complain when we talk about the probability of something that only will happen once? I actually don’t like getting into this philosophical discussion (Gelman has some thoughts worth reading) and I cut people who write for the masses some slack. If the aggregators consistently outperform the pundits in their predictions I have no problem with them using the word “probability” in their reports. I look forward to some of the post-election analysis of all this.

Nate Silver does it again! Will pundits finally accept defeat?

My favorite statistician did it again! Just like in 2008, he predicted the presidential election results almost perfectly. For those that don’t know, Nate Silver is the statistician that runs the fivethirtyeight blog. He combines data from hundreds of polls, uses historical data to weigh them appropriately and then uses a statistical model to run simulations and predict outcomes.

While the pundits were claiming the race was a “dead heat”, the day before the election Nate gave Obama a 90% chance of winning. Several pundits attacked Nate (some attacks were personal) for his predictions and demonstrated their ignorance of Statistics. Jeff wrote a nice post on this. The plot below demonstrates how great Nate’s prediction was. Note that each of the 45 states (including DC) for which he predicted a 90% probability or higher of winning for candidate A, candidate A won. For the other 6 states the range of percentages was 48-52%. If Florida goes for Obama he will have predicted every single state correctly.

Update: Congratulations also to Sam Wang (Princeton Election Consortium) and Simon Jackman (pollster) that also called the election perfectly. And thanks to the pollsters that provided the unbiased (on average) data used by all these folks. Data analysts won “experts” lost.

Update 2: New plot with data from here. Old graph here.