Month: November 2017

Correction Notice for Results

I was rechecking my error calculations after I received a comment about the error calculations from a reviewer of my paper.   An example calculation was incorrect.  This was a minor error, but a further examination led to the discovery of a error in the 2-Party error of my model in 2012.  The error was approximately half of what it was supposed to be.  This mistake made my model falsely appear more accurate than the Five Thirty Model do to this underestimation.   All of the error calculations are currently being reexamined for possible errors.  I have already recalculated all the errors but I want to check them a couple of more times to be safe.  A corrected table will be posted once it is checked again.

Update: 12/5

No other major errors were found in the re-checking process.  All calculations have been checked three times post the discovery of the error of the 2012 2-Party error for my model.

Update: 1/20  fixed typo in tested model 2008 for both all candidates and 2-party and adjusted average

Below is the updated table to replace the former tables used in both the ESR Virtual Poster and the USPROC Paper:

 

Tested Model RMSE Tested Model RMSE Swing States RCP RMSE Swing State 538 RMSE 538 RMSE Swing State
2008 All Candidates 3.5474 3.14788 4.23389 3.19332 1.66958
2008 -2 Party 2.89669 2.57051 3.63513 3.0305 1.47846
2012 All Candidates 3.25139 1.94492 2.33511 2.38019 1.2979
2012 2-Party 2.37053 1.17163 1.61076 1.98642 0.9342
2016 All Candidates 6.82013 3.95985 3.32952 5.37952 3.56511
2016 2-Party 3.95985 3.14325 2.04295 3.81296 2.31948
All Candidate  Average 4.53964 3.01755 3.299507 3.65101 2.17753
2-Party Average 3.07569 2.29513 2.42961 2.94329 1.57738
2-Party Average Compared to 538 0.95695 0.68727 0.64923
2-Party Compared to RCP 1.05859

 

Column1 Tested Model RMSE Tested Model RMSE SS RCP RMSE SS FiveThirtyEight Polls PlusRMSE 538 RMSE SS
2008 3.5474 3.14788 4.23389 3.19332 1.66958
2008 -2 Party 2.89669 2.57051 3.63513 3.0305 1.47846
2012 3.25139 1.94492 2.33511 2.38019 1.2979
2012 2-Party 2.37053 1.17163 1.61076 1.98642 0.9342
2016 6.82013 6.42335 8.23311 5.37952 4.14228
2016 2-Party 3.95985 3.03986 1.89412 3.81296 2.41263
2016 SS without UT and AZ 3.99534 3.32952 3.56511
2016 SS without UT and AZ 2-Party 3.14325 2.04295 2.31948
Overall Average 4.53964 3.83872 4.93404 3.65101 2.36992
2-Party Average 3.07569 2.26067 2.38 2.94329 1.60843
2 – Party Average Compared to 538 0.95695 0.71148 0.67581
2- Party Compared to RCP 1.05279

 

 

What a Pulmonary Embolism Taught Me About Statistics

On May 3rd, 2017, I was released from the hospital following an overnight stay for the treatment of a pulmonary embolism.  I am now almost fully recovered.  I think this experience is a great opportunity to teach statistics through a real-life example. I learned three things from this experience.

Vastly different fields can have the same underlying statistical processes

So far I have worked almost exclusively with political science data. My research is about how to estimate a proportion from a sample and how to compare it to other proportions.  When my doctor told me I might have a pulmonary embolism,  I wanted to see the data for myself.  So I read the journal articles, FDA case reports, and any data I could find to try to get an estimate for the chance I had a pulmonary embolism.  What I quickly realized is that the data about adverse drug reactions had similarities with the political science data I was familiar working with.   The data had issues with nonresponse bias and limitations due to unideal sample sizes.  Although political science and pharmacology are very different fields the share similar kinds of statistical problems.

Bayesian statistics is a powerful tool in many fields

Through this process, I saw how Bayesian statistics could help solve a difficult and important problem.  My doctor came by and saw me during my brief hospital stay.  She talked about how while she knew that it was unlikely any random woman in her twenties would have a pulmonary embolism,  but the details of my case suggested that the probability I had a pulmonary embolism was significant.  In short, the Bayesian mindset is about incorporating your prior beliefs and adapting them in the presence of additional information.  I don’t think my doctor used Bayes Theorem (the formal formula for estimating a probability given prior information), but she used Bayesian reasoning.  She had initial beliefs about the cause of my symptoms, and she updated her beliefs when she got new information  (like lab results).  This is probably normal reasoning for a doctor trying to diagnose a patient, but it showed me how Bayesian statistics could be applied to other fields.   A more formal use of Bayesian statistics would provide even better information to estimate probabilities.  I always knew Bayesian statistics could be useful in other cases besides politics, but this experience showed me a new area I am interested in researching.

 I am interested in other fields to apply statistics to besides politics

I wish I could have discovered my interest in biostatistics without a life-threating medical event, but I am glad.  I was exposed to a problem that is important and would use some of the same techniques I was exposed to during my work on political science.  While I still love political science statistics, I feel like I have now answered the question on what I can research in years where is no major election.  I enjoyed reading clinical trials and studies and analyzing their statistics.  Maybe someday I can even study how to improve statistical methods to prevent and diagnosis pulmonary embolisms like mine.

Six months after returning home from the hospital,  I am grateful that God has found a way to use my PE for good.