By Allan Fromen, PhD
“Donald Trump will just choke” Bill Kristol
“Republicans are not going to win this next election” Karl Rove
“I don’t know any consultant who privately believes that Donald Trump’s going to win” David Axelrod
It is now obvious to everyone that the so-called experts – the talking heads who are paid to share their opinions – all got it terribly wrong. The pundits were stunned when Trump won the election.
But I knew it would happen. In fact, I predicted a Trump presidency, and out-forecasted all the experts.
Don’t believe it? Allow me to demonstrate.
A year ago, I wrote The Psychology of Donald: What Marketers Can Learn From The Trump Campaign, which pointed out how Trump was successfully using “base level passions” in his campaign. The article ended with a reminder that “emotion outmaneuvers logic every time.”
This was very early in the process, when there were still over a dozen candidates for the Republican nomination. So I didn’t make a call outright, but I recognized Trump’s potential appeal early on.
Then, once Trump won the Republican nomination, I tweeted the following:
Superforecasting is a book that explains, among other things, just how awful experts are in predicting anything, even in their area of expertise. In this tweet, I am clearly skeptical of the pundits, and their forecast that Trump can’t win.
A week later, I tweeted:
This tweet suggests that the polls were artificially low for Trump due to social desirability, which is the well-known response bias where people will report inaccurately on sensitive topics in order to present themselves in the best possible light.
Then on September 26th, I posted Why Is This Election Even Close? Blame Stereotypes and System 1. The article posited that stereotypes, positive ones for Trump and negative ones for Clinton, were behind the election being as close it was, despite other signs that would seemingly indicate a clear Clinton path to victory.
Finally, on November 3rd, just a few days before the election, I tweeted this:
A Black Swan, popularized by Nassim Nicholas Taleb, is an unforeseen event. Essentially it is an occurrence that deviates beyond what is expected, and is extremely difficult to predict. The clear message in this tweet is, if the Cubs can win, so can Trump.
This last tweet, viewed collectively with the other tweets and posts, convincingly and irrefutably demonstrate how I predicted a Trump victory.
Except that I didn’t.
The above is an example of hindsight bias. This classic cognitive bias is a staple of behavioral economics and speaks to the tendency, after an event has occurred, to overestimate our own ability in having predicted an outcome. The “knew-it-all-along” effect, as it is also known, allows us to believe that we predicted something correctly, when in fact we did not.
Hindsight bias happens all the time, especially for those of us who watch sports (“I knew they should have kicked a field goal” after a failed fourth and 1 attempt, for example). But in the world of market research, hindsight bias can impact us directly.
Have you ever presented the results of a market research study, only to hear the client say something along the lines of “we already knew that” or “this confirms what we already suspected.” The client believes they knew it all along. But did they really? Most likely, they did not, and are instead suffering from hindsight bias.
One way to combat this, is to have clients state their hypotheses before the commencement of a research project. This is a useful exercise for all involved. By codifying hypotheses up front, it helps to frame the research, get everyone on the same page, and most importantly, defend the research from hindsight bias once delivered.
Hindsight bias makes us feel good, because it creates a sense of accomplishment and competence that we predicted something others did not. However, as Daniel Kahneman notes “Hindsight bias has pernicious effects on the evaluations of decision makers.” Let’s strive to constantly be vigilant, in our professional and personal lives, and not fall prey to the alluring charms of hindsight bias.