Shame on the New York Times and Their Phony, Baloney Polling Enterprises

By:

Dave Kingsley

   As a political scientist trained and experienced in statistics and psychometrics, I’m appalled by the by the lack of professionalism and scientific integrity in media polling enterprises such as the New York Times Quinnipiac and Siena college polls.  For instance, the contents of an article appearing in the New York Times today (October 11th) under this headline (above the fold): “New Poll Shows G.O.P. Edge to Seize Senate” is terribly irresponsible journalism.  These down and dirty little polls are nothing more than pseudoscience and charlatanism. They serve the need of print media to attract interest and sell advertising.  Unfortunately, they also influence public opinion and voting behavior.

    The polls at issue in the article pertain to the races of  Democrats John Tester in Montana, Collin Allred in Texas, and Debbie Marcasel-Powell in Florida.  Based on a faux-scientific “Times/Sienna” boiler room calling operation and some mindless mathematical calculations, the article headline on page A9 blares “New Poll Shows the G.O.P. With an Edge to Seize Control of the Senate.” 

    The implication is that Tester and Marcasel-Powell are finished, and, therefore, the Republicans will take back the Senate.  This article will certainly put some wind to the back of the Republicans.  A couple of hours after I read the article in my print edition, I saw and heard anchor person Anna Cabrera on MSNBC ask a field correspondent if “the race was slipping away from John Tester.”  He indicated that it indeed did seem that way.

    Commonsense should tell us that a poll accompanied by an article indicating that an election is all but settled a month out from that election discourages one side or the other  from supporting the predicted loser in the form of money and/or volunteer effort.  Worse than these effects on political behavior are  the noise and confusion injected into public discourse by the proliferation of unscientific polling businesses.

    The unbelievable increase in pollsters has poured a stream of noise into public discourse that contributes nothing to enlightenment regarding policy. Rather they cause unnecessary worry, anger, discouragement, and mindless conformity. The results of a recent study in the International Journal of Public Opinion suggests that poll results cause a “bandwagon effect” on voting behavior.  Other research validates this by showing that the normal human tendency to conform – often subliminally – leads to support for the candidate who is presented as the likely winner in polls.

    Pollsters add a patina of science to their nonsense by a mindless calculation they call the “margin of error.”  From the perspective of statistical theory, their understanding and interpretation of their simplistic calculations are laughable. Nevertheless, talking heads on every major news outlet parrot this nonsense by repeating something like “that is within the margin of error.” 

    NOTE:  See the post accompanying this post if you are interested in a slightly technical explanation of what pollsters call “margin of error.”  If you don’t want to take time to work through the explanation, I fully understand that.  You can take my word for it.  I’ve taught it.  I’ve applied it in my own research. And I can tell you with absolute certainty that the New York Times and their little-known colleges looking for branding are misusing statistical theory that has little to do with the kind of gross error involved in the political polling business.

    The New York Times isn’t engaged in science as they would have you believe. They are creating a narrative that they hope keeps you on the edge of your seat, your eyes on their newspaper (and advertising), and anxiously awaiting the next poll.  Nothing sells like fear and anxiety.

    How arrogant is it for journalists and media outlets to act as soothsayers and intermediaries in U.S. elections by predicting who is likely to win an election based on their faulty form of data collection and analysis?  Their job is to tell the truth as best they understand it.  They should not be engaged in reporting their own bad science and influencing elections.

    As Neil Postman warned us a few decades ago in his classic little book Amusing Ourselves to Death, “In the age of show business, public discourse will become dangerous nonsense.” And thus, it has.  So-called political polling does nothing more than add more noise in the stream of noise that now constitutes political discourse.  Noise becomes really dangerous when it is passed off as science.  In the current climate of scientific nihilism, any ridiculous claim to scientific legitimacy can be made palatable with numbers.  New York University Professor Journalism, Charles Seife, put it this way in his book Proofiness: The Dark Arts of Mathematical Deception: “If you want to get people to believe something really, really stupid, just stick a number on it.  Even the silliest absurdities seem plausible the moment that they’re expressed in numerical terms.”

    The Polling “Margin of Error:” Why Pollsters Claim They Get It Right When in Fact They Don’t

By:

Dave Kingsley

        How many times have you heard a talking head say that a poll result is “within the margin of error?”  It is important for the viewing audience or readers of print media to know that the media is merely parroting nonsense flowing from the polling industry, which is basically in the business of producing junk science.  Unfortunately, media representatives do not understand the simple calculations pertaining to the “margin of error” and how these calculations relate to mathematical theory. It is not the mindless calculations that are wrong.  Rather it is the statistical theory that requires serious thinking that is consistently ignored.

    Aside from the calculations, most explanations of “margin of error” I find on the internet are incorrect.  Talking heads and print journalists certainly do not understand what it means as they report ad nauseum, ad infinitum on pollsters’ never-ending supply of pseudoscience.

     The margin of error is of necessity utilized erroneously when it is applied to political polling.  MSM talking heads and writers don’t understand that the ME is based on theoretical, mathematical statistics and assumes specific conditions, which are not and cannot be met by pollsters. Let me explain.

    Researchers must have strong evidence that a sample from which responses are obtained is selected randomly and is a very close representative of the population from which it is obtained.  Given a scientifically suitable level of randomness, an error rate is estimated at a confidence level chosen by individual researchers.  Typically, confidence levels are set at 95%, which means that if the exact same population was resampled in the exact same manner 100 times, the true population mean would fall within each separate calculated confidence interval 95 out of 100 times.

    The theory from which this is derived is known as the “binomial standard error” which is simply [(p*q)/square root of n]*1.96.

    Where: P is the percentage responding to one option in a poll, Q is the percentage responding to the other option, and N is the number of individuals (respondents) providing a response. 

    The “1.96” value can be explained this way:

     If the poll were conducted 100 times in the exact same manner with respondents from the exact same population, the distribution of results, i.e., p and q would be normal, i.e., like a “bell curve” or what is mathematically known as a Gaussian Curve.  On can expect that under these conditions the population p will fall within ±1.96 standard deviations from the sample mean (p) in 95 out of 100 samples.  It is not the case that there is a 95% probability that the true population p is within ±1.96 standard deviations of the sample p.

    Example:  1,000 responses are obtained through calls to prospective respondents.  Let’s assume unrealistically that exactly 53 percent respond that they will vote for candidate A and 47 percent respond that they will vote for candidate B.  Further, let’s assume unrealistically that those responses are “random” and a representative sample of the population from which respondents are randomly selected. The ME for that sample would be:

    The correct mathematical interpretation of this result would be that the estimated true population mean for p is  .53 ±.031 or between .561 and .499 or, conversely one could assume that estimated  mean for q is .47±.031 or between .501 and .439.  However, that estimated p could be one of the 5 out of 100 samples in which the true population p falls outside of the estimated range. 

    In a presidential election, it is very easy but meaningless for a pollster to say they got it right. They hedge well because presidential elections tend to be close.  It is hard to imagine that the results would be 56.1 percent to 43.9 percent in any presidential elections held today.  Therefore, if pollsters overwhelming suggested that Hillary Clinton would win but she lost, they can always claim they weren’t wrong because the results fell within the margin of error.