Everybody Lies: Four Powers That Take Away Your Advantage in Big Data Surveys

Everybody Lies: The Hidden Truth About Everybody’s Relationships by Steven Pinker is one of those rare books that really does make the most of its subject matter. This isn’t just some personal story about somebody’s life. This isn’t a story about somebody’s failures or successes. This isn’t even about how much or how little someone earns. No, this book is about understanding why people lie to each other. Why the very act of lying itself causes such an emotional and psychological reaction in humans.

everybody lies big data

STEPHEN STEPHENS-DAVID OESS AND PETER FOX FORMED IT TO THE PUBLIC IN 1998: “There are at least four powers of lying: the conscious lie, the unconscious lie, the self-deception lie and the unconscious truth. Each lies have different consequences, depending on who lies and what kind of life they choose to tell.” [italics added] Now, here’s my take on these four powers of lying and why everybody lies.

The conscious lie involves what I call the status quo response. When people know they’re being tested, they lie just to avoid taking the blame or getting criticized for their behavior. In essence, they lie about their competence. This is called the status quo bias. Unfortunately, this lie leads to all kinds of social problems like inflated labor productivity, huge tax cuts for corporations and a reduction in government investments in public goods and services, etc.

The unconscious lie involves the systematic misrepresentation of facts by powerful groups for political or personal gain…or sometimes just for fun and relaxation. You can think of it as lying through your teeth, but with human faces. This type of lying is surprisingly common in social science research. “The bulk of scientists don’t tell you about whom they investigate, what data they collect, or what methods they use to analyze that data,” said Stephen Wolfram, professor of computer science at the University of Michigan. “The bulk of social scientists don’t even tell you about what they do with the data once they’ve gathered it.”

In addition, most of them don’t tell you about what they did with the big data once they’ve collected it. How come? Well, because most people would rather believe their colleagues and the journals than accept the fact that scientists are gathering, analyzing and interpreting data according to scientific methods. As a result, we end up with biased scientific journals, which only serve to reinforce the power of corporate or political power centers. Imagine if everybody published their conclusions – instead of everybody working together to analyze, interpret and communicate the results.

The second piece of advice that the authors provide in their book – “how to deal with the unexpected” – is very important. In this regard, everybody should take into account the potential for digital falsehoods when considering whether to publish a scientific paper or research report. For example, a headline saying “A scientific study says…” could actually be an explanation or correction for why “a scientific study says” instead of simply stating, “There was a statistically significant difference between groups at a one-sided t-test”. This kind of discrepancy could easily go unnoticed by the vast majority of researchers who would never make the slightest attempt to deceive or lie in a study like that, but that has been done with a good cause in mind – uncovering a real biological or physiological truth.

The other piece of advice that comes from the book, “How Everybody Lies: Big Data, Social Media, and the New Washington” by Stephen Dolan and Abraham Joshua Heschel, is even more direct. That is, rather than being a problem for big data scholars who want to publish and disseminate scientific findings, it is actually a problem for the scientists themselves if they allow their conclusions to be influenced by the power of social media. Consider, for example, the recently concluded investigation concerning the effects of violent movies on kids’ brains. Although most of the media was eager to jump on the bandwagon and promote a positive correlation, the press was careful to note that the finding was a correlation only, and not a direct, concrete one. The study had no experimental design, no control group, and no way to measure what kind of behavior was preying on the children in question – in other words, there simply wasn’t any real science to be done!

This is just one example that shows how important it is to consider all the ways in which large-scale organisations are able to shape the outcome of scientific experiments. In addition to this new types of problem for scientists looking to publish on this subject, it also means that we cannot rely on the old methods of blinding and so on. Science in this day and age are a very logistic place: once results have been published, you cannot easily reverse-control or even analyse why people have chosen a different conclusion. Therefore, we should all be prepared for a world where almost everyone lies, everyone cherry-pick, and everybody gambles.