It’s fashionable to say that journalists are politically biased. They are, but that’s not the point. Journalism is damaged by sampling bias.
Journalists report anecdotes as if a few scattered samples are an indication of statistical trends. Making matters worse, journalists select which events to cover and which to ignore, creating a distorted view of reality. They string together these anecdotes to form an artistic narrative to sell to audiences.
Journalists confuse randomness as meaningful. They insert emotional appeals and taint our interpretation. It’s a false narrative, obviously.
Even “Police Blotter” reporting suffers sample bias.
Violent Crime rates have plunged since the 1994. As a macrotrend, our crime rates are far lower than they were in the 1960s and 70s. Murders, Rapes, Robberies are all down to some of the lowest levels in modern history.
You don’t learn this information through the nightly news. They report a steady stream of crimes every week. Technically, the news doesn’t have time to cover every time that occurred, so they select an pick a handful of the more sensational ones. This is a biased sample and it gives a very misleading impression of the scale and intensity of crimes. Even though murder rates have dropped, news reporting of crimes remains the same or even increases.
What’s more, journalists do not count the non-criminal actions of the day. There could be fifty million interactions, of which 4 are violent. Journalists report on these exceptions, not the norm.
Not reporting the norm at all becomes dangerous.
Nassim Taleb, the author of Black Swan, describes how this distorts our impression of reality. Biased samples gives us a false impression of probability and uncertainty. People assign motivations and false explanations of randomness.
He divides our concept of thinking into two categories. There are Gaussian Statistics, such as the Bell Curve, which he calls type one. Type-2 randomness follows Pareto’s Law, which I talked about here.
Part of the problem is that journalists would have no idea what I’m talking about. They don’t even know who Gauss is. The lack of mathematics in the journalist profession destroys its credibility in my opinion.
The puzzling question is why is it that we humans don’t realize that we don’t know anything about the significant brand of randomness? Why don’t we realize that we are not that capable of predicting? Why don’t we notice the bias that causes us not to realize that we’re not learning from our experiences? Why do we still keep going as if we understand them?
We are not made for type-2 randomness. How can we humans take into account the role of uncertainty in our lives without moralizing? As Steve Pinker aptly said, our mind is made for fitness, not for truth — but fitness for a different probabilistic structure.
Which tricks work? Here is one: avoid the media. We are not rational enough to be exposed to the press. It is a very dangerous thing, because the probabilistic mapping we get from watching television is entirely different from the actual risks that we exposed to. If you watch a building burning on television, it’s going to change your attitude toward that risk regardless of its real actuarial value, no matter your intellectual sophistication. How can we live in a society in the twenty-first, twenty-second, or twenty-third century, while at the same time we have intuitions made for probably a hundred million years ago? How can we accept as a society that we are largely animals in our behavior, and that our understanding of matters is not of any large consequence in the way we act?
I avoid TV News as much as possible. I don’t trust it. Not so much any individual story, but I don’t trust the way it shapes my outlook towards society. It’s a distortion, like the funny mirrors in a circus.
He goes on.
People in the humanities tend to compound our biases — they do not understand the basic concept of sampling error. You also have the businesspeople and their servant economists. Gerd Gigerenzer, paraphrasing George Orwell, has noted that every human being needs to learn to read, to write, and to understand statistical significance. He gave examples showing that the third step has proven so much more difficult than the first two. This is where understanding the significance of events is something that cannot be done without the rigor of scientific skepticism.
On the right side you have Montaigne, worthy of respect because he’s intensely introspective, with the courage of resisting his own knowledge. That was before everything got wrecked with the Cartesian world, the quest for certainties, and encouraging people to explain. Add Hume, Popper, Hayek, Keynes, Peirce. In that category you also have the physicists and the scientists in the empirical world. Why? It’s not because they don’t have human biases, but because you have this huge infrastructure above them that prevents them from saying something that they cannot back up empirically. You also have many skeptical traders (or inverse-traders) in the right column, those who are aware of our inability to predict markets. We do not speculate but take advantage of imbalances and order-flow. We operate from a base of natural skepticism.
I think the last paragraph contains a very important point. Left unrestrained by empiricism, we all engage in wild speculation about things we do not understand. Mathematical rigor and skepticism of speculation brings us back to reality.
It’s my observation that scientists can be brilliant and empirically rigourous within their narrow speciality, but often wildly speculate about things they don’t understand, like economics, politics or religion. It’s not that science makes them into better men, it’s the infrastructure of science holds them in check.
Journalists lack this infrastructure. Political bias is the least of our problems. The concept of journalism is fundamentally flawed. I’m not sure what we can replace it with yet. Economics, yes, but it’s too dry to be a public service business. Human beings are very boring creatures, so perhaps we need artistic narratives to pretend we’re exciting.