Statistics are not cold hard facts – as Nate Silver writes in The Signal and the Noise (2012): ‘The numbers have no way of speaking for themselves. We speak for them. We imbue them with meaning.’ Not only has someone used extensive judgment in choosing what to measure, how to define crucial ideas, and to analyse them, but the manner in which they are communicated can utterly change their emotional impact. Let’s assume that £350 million is the actual weekly contribution to the EU. I often ask audiences to suggest what they would put on the side of the bus if they were on the Remain side. A standard option for making an apparently big number look small is to consider it as a proportion of an even bigger number: for example, the UK’s GDP is currently around £2.3 trillion, and so this contribution would comprise less than 1 per cent of GDP, around six months’ typical growth. An alternative device is to break down expenditure into smaller, more easily grasped units: for example, as there are 66 million people in the UK, £350 million a week is equivalent to around 75p a day, less than $1, say about the cost of a small packet of crisps (potato chips). If the bus had said: We each send the EU the price of a packet of crisps each day, the campaign might not have been so successful.

The second problem is that we are carrying out repeated significance tests, as each year’s new data are added and another test performed. Fortunately, it turns out that there is some remarkable but complex theory, delightfully known as ‘the law of the iterated logarithm’. This shows that if we carry out such repeated testing, even if the null hypothesis is true, then we are certain to eventually reject that null at any significance level we choose.

Fortunately, there are statistical methods for dealing with this problem of sequential testing. They were first developed in the Second World War by teams of statisticians working on industrial quality-control of armaments and other war materiel.

Armaments coming off the production line were being monitored by steadily accumulating total deviations from a standard, much in the same way as monitoring excess mortality. Scientists realised that the law of the iterated logarithm meant that repeated significance testing would always lead eventually to an alert that the industrial process had gone out of strict control, even if in truth everything was functioning fine. Essentially, if we keep on checking on a process, in the end something will look odd just by chance alone.

This last part reminds me of Buffet: "If a cop follows you for 500 miles, you're going to get a ticket”

From the eponymous Dunning of the Dunning-Kruger effect

In our work, we ask survey respondents if they are familiar with certain technical concepts from physics, biology, politics, and geography. A fair number claim familiarity with genuine terms like centripetal force and photon. But interestingly, they also claim some familiarity with concepts that are entirely made up, such as the plates of parallax, ultra-lipid, and cholarine. In one study, roughly 90 percent claimed some knowledge of at least one of the nine fictitious concepts we asked them about. In fact, the more well versed respondents considered themselves in a general topic, the more familiarity they claimed with the meaningless terms associated with it in the survey.

An ignorant mind is precisely not a spotless, empty vessel, but one that’s filled with the clutter of irrelevant or misleading life experiences, theories, facts, intuitions, strategies, algorithms, heuristics, metaphors, and hunches that regrettably have the look and feel of useful and accurate knowledge. This clutter is an unfortunate by-product of one of our greatest strengths as a species. We are unbridled pattern recognizers and profligate theorizers. Often, our theories are good enough to get us through the day, or at least to an age when we can procreate. But our genius for creative storytelling, combined with our inability to detect our own ignorance, can sometimes lead to situations that are embarrassing, unfortunate, or downright dangerous—especially in a technologically advanced, complex democratic society that occasionally invests mistaken popular beliefs with immense destructive power (See: crisis, financial; war, Iraq). As the humorist Josh Billings once put it, “It ain’t what you don’t know that gets you into trouble. It’s what you know for sure that just ain’t so.”

The way we traditionally conceive of ignorance—as an absence of knowledge—leads us to think of education as its natural antidote. But education, even when done skillfully, can produce illusory confidence. Here’s a particularly frightful example: Driver’s education courses, particularly those aimed at handling emergency maneuvers, tend to increase, rather than decrease, accident rates. They do so because training people to handle, say, snow and ice leaves them with the lasting impression that they’re permanent experts on the subject. In fact, their skills usually erode rapidly after they leave the course. And so, months or even decades later, they have confidence but little leftover competence when their wheels begin to spin.

In these Wild West settings, it’s best not to repeat common misbeliefs at all. Telling people that Barack Obama is not a Muslim fails to change many people’s minds, because they frequently remember everything that was said—except for the crucial qualifier “not.” Rather, to successfully eradicate a misbelief requires not only removing the misbelief, but filling the void left behind (“Obama was baptized in 1988 as a member of the United Church of Christ”). If repeating the misbelief is absolutely necessary, researchers have found it helps to provide clear and repeated warnings that the misbelief is false. I repeat, false.

Although widely held, the belief that merit rather than luck determines success or failure in the world is demonstrably false. This is not least because merit itself is, in large part, the result of luck. Talent and the capacity for determined effort, sometimes called ‘grit’, depend a great deal on one’s genetic endowments and upbringing.

In competitive contexts, many have merit, but few succeed. What separates the two is luck.

In addition to being false, a growing body of research in psychology and neuroscience suggests that believing in meritocracy makes people more selfish, less self-critical and even more prone to acting in discriminatory ways. Meritocracy is not only wrong; it’s bad.

Perhaps more disturbing, simply holding meritocracy as a value seems to promote discriminatory behaviour. [Researchers] found that, in companies that explicitly held meritocracy as a core value, managers assigned greater rewards to male employees over female employees with identical performance evaluations. This preference disappeared where meritocracy was not explicitly adopted as a value.

However, in addition to legitimation, meritocracy also offers flattery. Where success is determined by merit, each win can be viewed as a reflection of one’s own virtue and worth. Meritocracy is the most self-congratulatory of distribution principles.

Despite the moral assurance and personal flattery that meritocracy offers to the successful, it ought to be abandoned both as a belief about how the world works and as a general social ideal. It’s false, and believing in it encourages selfishness, discrimination and indifference to the plight of the unfortunate.