Tim Harford's Blog, page 52

The challenges of performing, online or off, in the covid age

The FT Weekend Festival, for the past few years a tented spectacular held at Kenwood House in London, is now a three-day online affair. Such are the times in which we live. It’s not all bad, of course: this week’s event boasts even more A-listers than usual and one can enjoy them from the comfort of an armchair. But the altered circumstances got me thinking about the challenges of performing, online or offline, in a post-Covid-19 world.

Things were simple — if dramatic — for a while. Theatres, music venues, corporate conferences and book festivals all shut down abruptly. Everything then turned into a Zoom video call, which was fine — for a while. When I spoke at the Hay Festival in May, there was a certain excitement about the sheer number of people who could watch. Even if my webcam wasn’t great, people would make allowances and enjoy a peek at my bookshelves.

But the problems quickly became apparent. For venues, performances over Zoom do not pay the rent, or the salaries of production teams and front-of-house staff. Even for performers themselves, it is hard to collect much income for live online work.

I have a series of talks coming up, but the fees have shrunk, or disappeared. That is manageable for me — after all, I am promoting my new book — but for many artists it is the CD, business book or comedy DVD that serves as advertising for more lucrative live performances, and not the other way around. And no one wants to pay much for a performance over Zoom.

One can improve the look and sound of an online performance by pre-recording it. But that way lies a danger: if the spontaneity of live work is removed, why wouldn’t audiences turn to archive recordings, produced without the constraints of lockdown?

During lockdown I was stunned by the brilliance of the National Theatre’s Frankenstein — recorded in 2011. I was moved to tears watching the Oslo Philharmonic orchestra perform Beethoven’s ninth Symphony on YouTube. It’s partly the music that gets me blubbing, but mostly the sight of a choir all signing together — as they were able to do, back in January 2019. None of this helps performers much today; indeed competition from the archives may actively harm them.

Another problem is that there is an art to online performance, and most of us are woefully short of practice. YouTubers, the experts, were the first to call attention to this. Matt Parker — mathematician, author, and a geek celebrity on YouTube — pointed out in March that when big-name talk-show hosts such as Seth Meyers and Stephen Colbert switched to presenting from their spare rooms, they used such poor equipment that they looked and sounded abysmal.

A serious YouTuber wouldn’t be caught dead using an inbuilt laptop webcam and microphone; a clip-on microphone and a proper camera deliver a vastly better performance. TV hosts quickly raised their game, but most of us are still staring down a nose-cam and sounding as if we’re in a bathroom.

As a matter of pride I have started using a professional camera, microphone and lighting. But such kit seems highly unlikely to be a profitable investment. No matter how fine a camera I get, I’m never going to look as good as anyone on RuPaul’s Drag Race or have special effects like The Mandalorian.

Now lockdown has eased, performers can get on stage again — this week the musical Sleepless opened in a London theatre. The problem is the audience: in a theatre, social distancing of two metres means about one seat in 10 can be filled — even a one-metre distance cuts capacity. And one has to wonder about the risk of infection over a two- or three-hour performance.

This is a problem both financially and artistically. I have found that even giving a talk about economics goes a lot better if everyone is packed in close, rather than lounging back around cabaret-style tables. But a thinned-out audience is better than none, and some performers are investigating “hybrid” events with a small physical audience who feel like VIPs and provide some atmosphere supplementing an online stream delivering more viewers and, perhaps, income.

Research is under way to figure out whether live events can restart safely. A couple of weeks ago, researchers in Germany held three successive concerts with several hundred volunteers, testing different conditions of crowding and hygiene, in the hope of assessing the risks. But results have not yet been published — and are hardly likely to be definitive.

None of this is ideal, leaving live entertainment in a desperate situation. The hard truth is that of all the things we do, crowding together for live events seems to pose one of the highest risks of infection, while being the easiest to substitute: just switch on the TV or stream some music.

We love live events and, once Covid-19 is defeated, I am confident that people will flock back. But for performers and production teams, full venues next year will put no food on the table today. It is going to be a hard road back.

Written for and first published in the Financial Times on 4 September 2020.

My NEW book How To Make The World Add Up is now out. Details, and to order on Hive, Blackwells, Amazon or Watersones. Stephen Fry comments, “Fabulously readable, lucid, witty and authoritative.”

A survival guide for the Covid age

August, 2020. The first wave of the coronavirus pandemic has passed in the UK, and we have all been trying to figure out what to do next. One friend cancelled a trip to see her family in Greece, too anxious to face the airports. Another was all for going out for pizza on a Friday night in a crowded pub, dismissing the “fuss” about the virus.

The risks from contracting Covid-19 vary enormously — by a factor of 10,000 between the age of nine and 90. But the perceived risk varies too — from those who are terrified to those who laugh the whole thing off as a hoax, occasionally with tragic consequences.

“What I want is a survival guide for life in the age of Covid,” says another friend. He’s in his early sixties. He’s barely left his London home since March, partly because he finds it commodious enough, but mostly because the risks of riding the underground seem to him to be simply too great. He is aware, though, that his instincts might be wrong.

So what do the data tell us about the risks of emerging from self-imposed isolation? That depends on where you live — and for more than one reason. Most obviously, the virus is much more prevalent in some places than others. Germany, Italy and the UK each have around 1,000 new confirmed cases a day. In the US there are 40,000 or more — which is equivalent to eight times as many, per person. In New Zealand by contrast there are just a handful. So one can afford to be more relaxed in Wellington than in Washington.

But the data also vary hugely in quality. In Singapore, South Korea or New Zealand, publicly available information helps citizens figure out if they have been in a high-risk location. Meanwhile, the US is having what Nature described this week as “a coronavirus data crisis”. There the numbers are either missing or late.

The UK occupies a middle ground. There is some data available about confirmed infections in each local area, but the most reliable information is national, thanks to the Office for National Statistics, which has been testing a representative sample of the English population as a whole — an essential exercise. This sample suggests that there were between 1,200 and 4,200 new infections a day in England in mid August. So the estimated rate of new infections in England, detected or undetected, is between 22 and 76 per million people per day, with a best guess of 44.

The typical English resident, then, has a 44 in a million chance each day of being infected. In the US, the midpoint of epidemiological models suggests around 150,000 new infections a day, or 450 per million people per day, about 10 times the risk in England. In South Korea, despite a recent spike in confirmed cases, the risk of infection is probably closer to 1 or 2 per million people per day. Those averages encompass people who take the utmost precautions, people who work in exposed professions or frequent house parties and everyone in between.

As a recent review in the British Medical Journal reminded us, crowding, shouting, not wearing masks, poor ventilation, and length of exposure time to others who may be infected all increase the chance of transmission. So I can only guess how much my friend’s risk increases when he decides to venture outside his door.

Still, knowing the current average infection risk — 44 in a million, per day — provides a sense of proportion. The virus itself is dangerous but no death sentence: for someone in his early sixties the chance of death, given infection, is similar to the population as a whole, at about 1 per cent. Lasting symptoms also seem to affect around 1 per cent of people.

Two per cent of 44 in a million is about one in a million. This rough estimate is a convenient enough number, even if something of a cliché in the world of risk. For my friend, Covid-19 therefore currently presents a background risk of a one in a million chance of death or lasting harm, every day. The risk of death alone is one in 2m.

To double-check this very rough estimate, I looked at registrations of deaths. In one week in mid-August, 139 people died in England and Wales with Covid mentioned on their death certificate. That is 20 a day — less than one death in 2m per day. The numbers add up.

I find it helpful to convert small risks into “micromorts” — a micromort being a one in a million chance of dying. Contracting Covid is risky: perhaps 10,000 micromorts on average, varying hugely depending on age. But simply existing in a country where the virus is suppressed but circulating is not so risky. It depends on age, gender, geography, behaviour and much else. But on average it is half a micromort a day — similar to going skiing, taking a short motorbike ride, or taking baths for a year – and considerably less risky than a scuba dive or a skydive.

My friend will have to make his own decisions, as we all will. But the risk to most individuals in the UK seems modest, for now. What worries me deeply is something different: the prospect of the virus surging back. We cannot afford to relax just yet, because we will be walking a Covid tightrope this autumn.

Written for and first published in the Financial Times on 28 August 2020. The original FT version contained an error; I wrongly compared the risk of covid to the risk of taking a bath, rather than the annual risk of dying in the bath. I apologise. If you want to read more about how I made that mistake and what happened next, I explain on Twitter here.

My NEW book How To Make The World Add Up is OUT NOW!

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Statistics, lies and the virus: five lessons from a pandemic

My new book, “How To Make The World Add Up“, is published today in the UK and around the world (except US/Canada).

Will this year be 1954 all over again? Forgive me, I have become obsessed with 1954, not because it offers another example of a pandemic (that was 1957) or an economic disaster (there was a mild US downturn in 1953), but for more parochial reasons. Nineteen fifty-four saw the appearance of two contrasting visions for the world of statistics — visions that have shaped our politics, our media and our health. This year confronts us with a similar choice.

The first of these visions was presented in How to Lie with Statistics, a book by a US journalist named Darrell Huff. Brisk, intelligent and witty, it is a little marvel of numerical communication. The book received rave reviews at the time, has been praised by many statisticians over the years and is said to be the best-selling work on the subject ever published. It is also an exercise in scorn: read it and you may be disinclined to believe a number-based claim ever again.

There are good reasons for scepticism today. David Spiegelhalter, author of last year’s The Art of Statistics, laments some of the UK government’s coronavirus graphs and testing targets as “number theatre”, with “dreadful, awful” deployment of numbers as a political performance.

“There is great damage done to the integrity and trustworthiness of statistics when they’re under the control of the spin doctors,” Spiegelhalter says. He is right. But we geeks must be careful — because the damage can come from our own side, too.

For Huff and his followers, the reason to learn statistics is to catch the liars at their tricks. That sceptical mindset took Huff to a very unpleasant place, as we shall see. Once the cynicism sets in, it becomes hard to imagine that statistics could ever serve a useful purpose. 

But they can — and back in 1954, the alternative perspective was embodied in the publication of an academic paper by the British epidemiologists Richard Doll and Austin Bradford Hill. They marshalled some of the first compelling evidence that smoking cigarettes dramatically increases the risk of lung cancer. The data they assembled persuaded both men to quit smoking and helped save tens of millions of lives by prompting others to do likewise. This was no statistical trickery, but a contribution to public health that is almost impossible to exaggerate. 

You can appreciate, I hope, my obsession with these two contrasting accounts of statistics: one as a trick, one as a tool. Doll and Hill’s painstaking approach illuminates the world and saves lives into the bargain. Huff’s alternative seems clever but is the easy path: seductive, addictive and corrosive. Scepticism has its place, but easily curdles into cynicism and can be weaponised into something even more poisonous than that.

The two worldviews soon began to collide. Huff’s How to Lie with Statistics seemed to be the perfect illustration of why ordinary, honest folk shouldn’t pay too much attention to the slippery experts and their dubious data. Such ideas were quickly picked up by the tobacco industry, with its darkly brilliant strategy of manufacturing doubt in the face of evidence such as that provided by Doll and Hill.

As described in books such as Merchants of Doubt by Erik Conway and Naomi Oreskes, this industry perfected the tactics of spreading uncertainty: calling for more research, emphasising doubt and the need to avoid drastic steps, highlighting disagreements between experts and funding alternative lines of inquiry. The same tactics, and sometimes even the same personnel, were later deployed to cast doubt on climate science. These tactics are powerful in part because they echo the ideals of science. It is a short step from the Royal Society’s motto, “nullius in verba” (take nobody’s word for it), to the corrosive nihilism of “nobody knows anything”. 

So will 2020 be another 1954? From the point of view of statistics, we seem to be standing at another fork in the road. The disinformation is still out there, as the public understanding of Covid-19 has been muddied by conspiracy theorists, trolls and government spin doctors.  Yet the information is out there too. The value of gathering and rigorously analysing data has rarely been more evident. Faced with a complete mystery at the start of the year, statisticians, scientists and epidemiologists have been working miracles. I hope that we choose the right fork, because the pandemic has lessons to teach us about statistics — and vice versa — if we are willing to learn.

1: The numbers matter

“One lesson this pandemic has driven home to me is the unbelievable importance of the statistics,” says Spiegelhalter. Without statistical information, we haven’t a hope of grasping what it means to face a new, mysterious, invisible and rapidly spreading virus. Once upon a time, we would have held posies to our noses and prayed to be spared; now, while we hope for advances from medical science, we can also coolly evaluate the risks.

Without good data, for example, we would have no idea that this infection is 10,000 times deadlier for a 90-year-old than it is for a nine-year-old — even though we are far more likely to read about the deaths of young people than the elderly, simply because those deaths are surprising. It takes a statistical perspective to make it clear who is at risk and who is not.

Good statistics, too, can tell us about the prevalence of the virus — and identify hotspots for further activity. Huff may have viewed statistics as a vector for the dark arts of persuasion, but when it comes to understanding an epidemic, they are one of the few tools we possess.

2: Don’t take the numbers for granted

But while we can use statistics to calculate risks and highlight dangers, it is all too easy to fail to ask the question “Where do these numbers come from?” By that, I don’t mean the now-standard request to cite sources, I mean the deeper origin of the data.

For all his faults, Huff did not fail to ask the question. He retells a cautionary tale that has become known as “Stamp’s Law” after the economist Josiah Stamp — warning that no matter how much a government may enjoy amassing statistics, “raise them to the nth power, take the cube root and prepare wonderful diagrams”, it was all too easy to forget that the underlying numbers would always come from a local official, “who just puts down what he damn pleases”.

The cynicism is palpable, but there is insight here too. Statistics are not simply downloaded from an internet database or pasted from a scientific report. Ultimately, they came from somewhere: somebody counted or measured something, ideally systematically and with care. These efforts at systematic counting and measurement require money and expertise — they are not to be taken for granted.

In my new book, How to Make the World Add Up, I introduce the idea of “statistical bedrock” — data sources such as the census and the national income accounts that are the results of painstaking data collection and analysis, often by official statisticians who get little thanks for their pains and are all too frequently the target of threats, smears or persecution.

In Argentina, for example, long-serving statistician Graciela Bevacqua was ordered to “round down” inflation figures, then demoted in 2007 for producing a number that was too high. She was later fined $250,000 for false advertising — her crime being to have helped produce an independent estimate of inflation.

In 2011, Andreas Georgiou was brought in to head Greece’s statistical agency at a time when it was regarded as being about as trustworthy as the country’s giant wooden horses. When he started producing estimates of Greece’s deficit that international observers finally found credible, he was prosecuted for his “crimes” and threatened with life imprisonment. Honest statisticians are braver — and more invaluable — than we know. 

In the UK, we don’t habitually threaten our statisticians — but we do underrate them.

“The Office for National Statistics is doing enormously valuable work that frankly nobody has ever taken notice of,” says Spiegelhalter, pointing to weekly death figures as an example. “Now we deeply appreciate it.” 

Quite so. This statistical bedrock is essential, and when it is missing, we find ourselves sinking into a quagmire of confusion.

The foundations of our statistical understanding of the world are often gathered in response to a crisis. For example, nowadays we take it for granted that there is such a thing as an “unemployment rate”, but a hundred years ago nobody could have told you how many people were searching for work. Severe recessions made the question politically pertinent, so governments began to collect the data. More recently, the financial crisis hit. We discovered that our data about the banking system was patchy and slow, and regulators took steps to improve it.

So it is with the Sars-Cov-2 virus. At first, we had little more than a few data points from Wuhan, showing an alarmingly high death rate of 15 per cent — six deaths in 41 cases. Quickly, epidemiologists started sorting through the data, trying to establish how exaggerated that case fatality rate was by the fact that the confirmed cases were mostly people in intensive care.

Quirks of circumstance — such as the Diamond Princess cruise ship, in which almost everyone was tested — provided more insight. Johns Hopkins University in the US launched a dashboard of data resources, as did the Covid Tracking Project, an initiative from the Atlantic magazine. An elusive and mysterious threat became legible through the power of this data.  That is not to say that all is well.

Nature recently reported on “a coronavirus data crisis” in the US, in which “political meddling, disorganization and years of neglect of public-health data management mean the country is flying blind”.  Nor is the US alone. Spain simply stopped reporting certain Covid deaths in early June, making its figures unusable. And while the UK now has an impressively large capacity for viral testing, it was fatally slow to accelerate this in the critical early weeks of the pandemic. Ministers repeatedly deceived the public about the number of tests being carried out by using misleading definitions of what was happening. For weeks during lockdown, the government was unable to say how many people were being tested each day.

Huge improvements have been made since then. The UK’s Office for National Statistics has been impressively flexible during the crisis, for example in organising systematic weekly testing of a representative sample of the population. This allows us to estimate the true prevalence of the virus. Several countries, particularly in east Asia, provide accessible, usable data about recent infections to allow people to avoid hotspots.

These things do not happen by accident: they require us to invest in the infrastructure to collect and analyse the data. On the evidence of this pandemic, such investment is overdue, in the US, the UK and many other places.

3: Even the experts see what they expect to see

Jonas Olofsson, a psychologist who studies our perceptions of smell, once told me of a classic experiment in the field. Researchers gave people a whiff of scent and asked them for their reactions to it. In some cases, the experimental subjects were told: “This is the aroma of a gourmet cheese.” Others were told: “This is the smell of armpits.” In truth, the scent was both: an aromatic molecule present both in runny cheese and in bodily crevices. But the reactions of delight or disgust were shaped dramatically by what people expected.

Statistics should, one would hope, deliver a more objective view of the world than an ambiguous aroma. But while solid data offers us insights we cannot gain in any other way, the numbers never speak for themselves. They, too, are shaped by our emotions, our politics and, perhaps above all, our preconceptions. There is great damage done to the integrity and trustworthiness of statistics when they’re under the control of the spin doctors

A striking example is the decision, on March 23 this year, to introduce a lockdown in the UK. In hindsight, that was too late. “Locking down a week earlier would have saved thousands of lives,” says Kit Yates, author of The Maths of Life and Death — a view now shared by influential epidemiologist Neil Ferguson and by David King, chair of the “Independent Sage” group of scientists.

The logic is straightforward enough: at the time, cases were doubling every three to four days. If a lockdown had stopped that process in its tracks a week earlier, it would have prevented two doublings and saved three-quarters of the 65,000 people who died in the first wave of the epidemic, as measured by the excess death toll.

That might be an overestimate of the effect, since people were already voluntarily pulling back from social interactions. Yet there is little doubt that if a lockdown was to happen at all, an earlier one would have been more effective. And, says Yates, since the infection rate took just days to double before lockdown but long weeks to halve once it started, “We would have got out of lockdown so much sooner . . . Every week before lockdown cost us five to eight weeks at the back end of the lockdown.”

Why, then, was the lockdown so late? No doubt there were political dimensions to that decision, but senior scientific advisers to the government seemed to believe that the UK still had plenty of time. On March 12, prime minister Boris Johnson was flanked by Chris Whitty, the government’s chief medical adviser, and Patrick Vallance, chief scientific adviser, in the first big set-piece press conference.

Italy had just suffered its 1,000th Covid death and Vallance noted that the UK was about four weeks behind Italy on the epidemic curve. With hindsight, this was wrong: now that late-registered deaths have been tallied, we know that the UK passed the same landmark on lockdown day, March 23, just 11 days later.  It seems that in early March the government did not realise how little time it had.

As late as March 16, Johnson declared that infections were doubling every five to six days. The trouble, says Yates, is that UK data on cases and deaths suggested that things were moving much faster than that, doubling every three or four days — a huge difference. What exactly went wrong is unclear — but my bet is that it was a cheese-or-armpit problem. Some influential epidemiologists had produced sophisticated models suggesting that a doubling time of five to six days seemed the best estimate, based on data from the early weeks of the epidemic in China.

These models seemed persuasive to the government’s scientific advisers, says Yates: “If anything, they did too good a job.” Yates argues that the epidemiological models that influenced the government’s thinking about doubling times were sufficiently detailed and convincing that when the patchy, ambiguous, early UK data contradicted them, it was hard to readjust. We all see what we expect to see.

The result, in this case, was a delay to lockdown: that led to a much longer lockdown, many thousands of preventable deaths and needless extra damage to people’s livelihoods. The data is invaluable but, unless we can overcome our own cognitive filters, the data is not enough.

4: The best insights come from combining statistics with personal experience

The expert who made the biggest impression on me during this crisis was not the one with the biggest name or the biggest ego. It was Nathalie MacDermott, an infectious-disease specialist at King’s College London, who in mid-February calmly debunked the more lurid public fears about how deadly the new coronavirus was. Then, with equal calm, she explained to me that the virus was very likely to become a pandemic, that barring extraordinary measures we could expect it to infect more than half the world’s population, and that the true fatality rate was uncertain but seemed to be something between 0.5 and 1 per cent. In hindsight, she was broadly right about everything that mattered.

MacDermott’s educated guesses pierced through the fog of complex modelling and data-poor speculation. I was curious as to how she did it, so I asked her.

“People who have spent a lot of their time really closely studying the data sometimes struggle to pull their head out and look at what’s happening around them,” she said. “I trust data as well, but sometimes when we don’t have the data, we need to look around and interpret what’s happening.”

MacDermott worked in Liberia in 2014 on the front line of an Ebola outbreak that killed more than 11,000 people. At the time, international organisations were sanguine about the risks, while the local authorities were in crisis. When she arrived in Liberia, the treatment centres were overwhelmed, with patients lying on the floor, bleeding freely from multiple areas and dying by the hour.

The horrendous experience has shaped her assessment of subsequent risks: on the one hand, Sars-Cov-2 is far less deadly than Ebola; on the other, she has seen the experts move too slowly while waiting for definitive proof of a risk.

“From my background working with Ebola, I’d rather be overprepared than underprepared because I’m in a position of denial,” she said.

There is a broader lesson here. We can try to understand the world through statistics, which at their best provide a broad and representative overview that encompasses far more than we could personally perceive. Or we can try to understand the world up close, through individual experience. Both perspectives have their advantages and disadvantages.

Muhammad Yunus, a microfinance pioneer and Nobel laureate, has praised the “worm’s eye view” over the “bird’s eye view”, which is a clever sound bite. But birds see a lot too. Ideally, we want both the rich detail of personal experience and the broader, low-resolution view that comes from the spreadsheet. Insight comes when we can combine the two — which is what MacDermott did.

5: Everything can be polarised

Reporting on the numbers behind the Brexit referendum, the vote on Scottish independence, several general elections and the rise of Donald Trump, there was poison in the air: many claims were made in bad faith, indifferent to the truth or even embracing the most palpable lies in an effort to divert attention from the issues. Fact-checking in an environment where people didn’t care about the facts, only whether their side was winning, was a thankless experience.

For a while, one of the consolations of doing data-driven journalism during the pandemic was that it felt blessedly free of such political tribalism. People were eager to hear the facts after all; the truth mattered; data and expertise were seen to be helpful. The virus, after all, could not be distracted by a lie on a bus. 

That did not last. America polarised quickly, with mask-wearing becoming a badge of political identity — and more generally the Democrats seeking to underline the threat posed by the virus, with Republicans following President Trump in dismissing it as overblown. The prominent infectious-disease expert Anthony Fauci does not strike me as a partisan figure — but the US electorate thinks otherwise. He is trusted by 32 per cent of Republicans and 78 per cent of Democrats.

The strangest illustration comes from the Twitter account of the Republican politician Herman Cain, which late in August tweeted: “It looks like the virus is not as deadly as the mainstream media first made it out to be.” Cain, sadly, died of Covid-19 in July — but it seems that political polarisation is a force stronger than death.

Not every issue is politically polarised, but when something is dragged into the political arena, partisans often prioritise tribal belonging over considerations of truth. One can see this clearly, for example, in the way that highly educated Republicans and Democrats are further apart on the risks of climate change than less-educated Republicans and Democrats. Rather than bringing some kind of consensus, more years of education simply seem to provide people with the cognitive tools they require to reach the politically convenient conclusion. From climate change to gun control to certain vaccines, there are questions for which the answer is not a matter of evidence but a matter of group identity.

In this context, the strategy that the tobacco industry pioneered in the 1950s is especially powerful. Emphasise uncertainty, expert disagreement and doubt and you will find a willing audience. If nobody really knows the truth, then people can believe whatever they want.

All of which brings us back to Darrell Huff, statistical sceptic and author of How to Lie with Statistics. While his incisive criticism of statistical trickery has made him a hero to many of my fellow nerds, his career took a darker turn, with scepticism providing the mask for disinformation. Huff worked on a tobacco-funded sequel, How to Lie with Smoking Statistics, casting doubt on the scientific evidence that cigarettes were dangerous. (Mercifully, it was not published.) 

Huff also appeared in front of a US Senate committee that was pondering mandating health warnings on cigarette packaging. He explained to the lawmakers that there was a statistical correlation between babies and storks (which, it turns out, there is) even though the true origin of babies is rather different. The connection between smoking and cancer, he argued, was similarly tenuous. 

Huff’s statistical scepticism turned him into the ancestor of today’s contrarian trolls, spouting bullshit while claiming to be the straight-talking voice of common sense. It should be a warning to us all.

There is a place in anyone’s cognitive toolkit for healthy scepticism, but that scepticism can all too easily turn into a refusal to look at any evidence at all.

This crisis has reminded us of the lure of partisanship, cynicism and manufactured doubt. But surely it has also demonstrated the power of honest statistics. Statisticians, epidemiologists and other scientists have been producing inspiring work in the footsteps of Doll and Hill. I suggest we set aside How to Lie with Statistics and pay attention.

Carefully gathering the data we need, analysing it openly and truthfully, sharing knowledge and unlocking the puzzles that nature throws at us — this is the only chance we have to defeat the virus and, more broadly, an essential tool for understanding a complex and fascinating world.

Written for and published by the FT Magazine on 10 September 2020.

My new book, “How To Make The World Add Up“, is published today in the UK and around the world (except US/Canada).

Don’t rely on algorithms to make life-changing decisions

NEWS! My new book, “How To Make The World Add Up“, is out tomorrow around the world (except US / Canada). Ordering a copy early, online or from your local bookshop is enormously helpful: it prods review interest, encourages physical bookshops to order and display the book, and so I am especially grateful forearly orders. More information here – including a chance to buy signed copies.

The governments of England and Scotland have fed the hopes and dreams of students into a paper shredder, yanked out the tatters and handed them to university administrators with instructions to tape everything back together. The fiasco of algorithmically assigned exam grades is a nightmare for pupils, a huge embarrassment for those in charge and should be a cautionary tale for the rest of the world. With all too many classes cancelled in recent months, here, at least, is a teachable moment, with lessons that go far beyond education.

To summarise the train wreck: with schools closed and exams cancelled, but grades needed to assign places at university, pupils were promised that results would be forthcoming. The final grades were assigned by a data-driven view of each school’s historical record. No student, no matter how outstanding, would be awarded top marks if the algorithm concluded that her or his school was not a top-grade-sort-of-place. Countless individual injustices resulted.

To add insult, students sitting niche subjects in small classes were spared the harsh discipline of the algorithm. Because private schools benefited disproportionately from this selective indulgence it looked as though a government full of over-privileged dimwits was using an algorithm to favour the over-privileged dimwits of the future.

The U-turn part of the algoshambles was to wait until universities had reassigned their offers and only then cancel the downgrades in favour of the original school predictions, which were significantly higher. With many students suddenly entitled to university places that no longer existed, the entire rolling dumpster fire has now been pushed at university admissions offices.

The injustice and the incompetence here is palpable enough. But there is a deeper point about the intoxicating charm of algorithms. In March, when the government faced agonising choices about schools and exams, the algorithm promised fast, effective pain relief through the miracle of modern technology.

“When faced with a difficult question, we often answer an easier one instead, usually without noticing the substitution,” writes the psychologist Daniel Kahneman, in Thinking, Fast and Slow.

The difficult question here was: could we give students the grades they would have earned in the exams?

The easier substitute was: could we make the overall pattern of exam results this year look the same as usual? That’s not hard. An algorithm could mimic any historical pattern you like — or ensure equality (within the limits of arithmetic) based on gender or race.

But note the substitution of the easy question for hard. It is impossible to give students the right grades for exams they never sat — one would have to be infatuated with algorithmic miracles not to realise that.

But infatuation with algorithmic miracles is not new. One of the first computer dating services was called Operation Match. In the mid-1960s, it promised that the computer would “scientifically find the right date for you”. In fact, it mostly matched people who lived near each other.

An algorithm might try to predict romantic compatibility, but a wise couple would not marry on that basis without meeting. An algorithm might try to predict who will commit crime, and we might focus support on that basis. But I hope we will never dare to jail people for algorithmically predicted pre-crimes.

Neither should we make the life-changing decision to deny a university place for the pre-crime of presumptively missing a grade in a hypothetical exam.

Perhaps the least bad option this year was to focus on maximising access to jobs, apprenticeships and higher education, cramming more students into universities rather than playing the game of fantasy grades. Regardless, we would all be in a better position now if back in March the government had faced the truth rather than being dazzled by the sparkling promise of the algorithm.

Written for and first published in the Financial Times on 21 August 2020.

My NEW book How To Make The World Add Up is OUT TODAY! And of course it has a chapter on being sensible about the strengths and limits of algorithms…

Details, and to order signed copies from MathsGear, or from Hive, Blackwells, Amazon or Watersones.

Stephen Fry comments, “Fabulously readable, lucid, witty and authoritative.”

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Cautionary Tales – Storks, Smoking and the Power of Doubt

My new book, “How To Make The World Add Up“, is out tomorrow around the world (except US / Canada).

Ordering a copy early, online or from your local bookshop is enormously helpful: it prods review interest, encourages physical bookshops to order and display the book, and so I am especially grateful forearly orders. More information here – including a chance to buy signed copies.

Meanwhile – enjoy this mini-episode of Cautionary Tales, inspired by my book, produced by Ryan Dilley and with music and sound design by Pascal Wyse. We are all back in the studio working hard on a mammoth 14-episode second series; stay tuned.

We won’t remember much of what we did in the pandemic

NEWS! My new book, “How To Make The World Add Up“, is out next week around the world (except US / Canada). Pre-ordering a copy online or from your local bookshop is enormously helpful: it prods review interest, encourages physical bookshops to order and display the book, and so I am especially grateful for pre-orders. More information here – including a chance to order signed copies.

When my mind wanders these days, I’ve noticed that it wanders to odd places — namely, far-off hotel rooms. Zurich, late last summer: the hotel was on the wrong side of the tracks but the room had big windows on two walls. Dallas, a few years back: the hotel had a huge atrium with a model railway; I ironed my shirt while listening to a podcast about the late-blooming composer Leoš Janáček. Rancho Mirage, January: the room was a sunny stroll away from reception; the pool looked tempting beside the desert palms, but was cold and full of leaves.

Why, of all things, would my mind jump to distant hotels? Not because anything much happened there: sadly, I must confess never to having done much of interest in a hotel room. Evidently, my memory is working in curious ways.

Last spring, I returned from the holiday of a lifetime in Japan, and reflected on the richness of the memories it had generated. Time flew by while I was there, but in hindsight 10 days somewhere vividly new had produced more memories than 10 weeks back home. I likened the effect to the compression of a film. Instead of storing each frame separately, video compression algorithms will start with the first frame of a scene and then store a series of “diffs” — changes from one frame to the next. A slow, contemplative movie with long scenes and fixed cameras can be compressed more than a fast-moving action flick.

Similarly, a week full of new experiences will seem longer in retrospect. A month of repeating the same routine might seem endless, but will be barely a blip in the memory: the “diffs” are not significant enough for the brain to bother with.

After months of working from home, I now realise that there was something incomplete about this account. New experiences are indeed important for planting a rich crop of memories. But, by itself, that is not enough. A new physical space seems to be important if our brains are to pay attention.

The Covid-19 lockdown, after all, was full of new experiences. Some were grim: I lost a friend to the disease; I smashed my face up in an accident; we had to wear masks and avoid physical contact and worry about where the next roll of toilet paper was coming from. Some were more positive: the discovery of new pleasures, the honing of new skills, the overcoming of new challenges.

But I doubt I am alone in finding that my memory of the lockdown months is rather thin. No matter how many new people or old friends you talk to on Zoom or Skype, they all start to smear together because the physical context is monotonous: the conversations take place while one sits in the same chair, in the same room, staring at the same computer screen.

The psychologist Barbara Tversky, author of Mind in Motion, argues that our minds are built on a foundation of cognition about place, space and movement. That creeps into our language with phrases such as “built on a foundation” and “creeps into”. Our brains started by helping us process our surroundings and the threats and opportunities they presented. Abstract thinking is an adaptation of those basic spatial capacities.

This may be why not all novelty is created alike. Our brains seem to record a new place with a particular vividness. There was nothing especially novel about ironing a shirt while listening to a podcast, and nothing especially intriguing about that Dallas hotel room. But it was a physical space in which I had never been before. That was enough to set my brain taking notes.

Even when a moment has nothing to do with place and everything to do with intellectual or emotional novelty, place still registers. When I think about important intellectual or emotional junctures in my life — from the first time I understood Arrow’s impossibility theorem to the first time I met my wife-to-be — even then, those memories are tied to a physical location.

No wonder champion mnemonists often use the “method of loci” or the “memory palace” technique, memorising long lists of abstract information by picturing vivid images in well-defined spaces, such as the rooms of a childhood home.

I’ve come to realise with renewed force the value of a pre-Covid habit: seeking out new places in which to read and to write, even something as simple as a new café or a new library. Fresher ideas and clearer memories come when one works somewhere different: in a new place, the mind is more alert.

This may be why, when we ask people to recall pivotal moments in history such as the fall of the Berlin Wall or the 9/11 terror attacks in Manhattan, we ask “where were you when you heard?”

Covid-19 may be as significant an episode as any, but it will not trigger the same sharp memories. Where were you during the pandemic? At home. For months. And without a physical change of scene, even new experiences all start to seem the same.

“You need to get out more,” someone once admonished me. She was right. These days, we all do.

Written for and first published in the Financial Times on 14 August 2020.

My new book How To Make The World Add Up is out VERY soon. Details, and to order on Hive, Blackwells, Amazon or Watersones.

Stephen Fry comments, “Fabulously readable, lucid, witty and authoritative.”

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I’m speaking (online, mostly) – tune in!

Upcoming events in support of the “How To Make The World Add Up” book tour.

Saturday 5th September, 7,10pm BST The Data Detectives – a session with FT data visualisation wizard, John Burn-Murdoch, at the FT Weekend Festival online.

Monday 7th September, 2.35pm BST – recording a special epsiode of Stats + Stories as a live session at the Royal Statistical Society annual conference, online. (You should be able to listen to the podcast in due course.)

Friday 11th September, 10am BST – giving a seminar to a live audience (what a pleasure that will be) at the Reuters Institute for the Study of Journalism Summer School. The session is open to all and streamed online.

Monday 14th September, 6pm BST – with David Spiegelhalter and Hannah Fry, at Intelligence Squared online.

Tuesday 15th September, 7pm BST – speaking at 5×15 online, alongside Laura Bates (“Everyday Sexism”), Lemn Sissay, The Secret Barrister, and Waad Al-Kateab.

Thursday 17th September, 6pm BST, hosted by LSE online.

NEW Monday 21st September 9am BST – hosted by the Institute of Directors.

Watch this space for more talks!

We fall in love with the new, but not everything old is obsolete

The Boeing 747 took another step towards retirement recently. British Airways, the operator of the largest fleet of passenger 747s, announced that the distinctive aeroplane would not be returning to service after the pandemic. For all the rightful concern about the environmental cost of long-haul travel, the plane will be missed by passengers and pilots alike.

Mark Vanhoenacker, pilot and writer, describes the plane as “370 tonnes of aviation legend”. The first time I rode on the top deck of a 747, my own excitement was more childlike — but still surely justified. Modified 747s carried the Space Shuttle around on their backs, and have served as the official plane for US presidents since the time of George HW Bush. It is an iconic design.

But what is often overlooked is the sheer longevity of the 747. The plane first flew before the 1969 Moon landing, and the British Airways announcement is hardly the end. The travel downturn caused by coronavirus may have accelerated its phasing out but new 747s are still being made as freight hauliers. That may soon stop but today’s planes are likely to still be flying in 2040 — and perhaps the 747 will even fly as an octogenarian in 2050.

We are used to replacing laptops after three or four years, and phones even more frequently. A computer first used in 1969 would be a museum piece now. Yet many technologies last much longer, as another museum piece attests. London’s Science Museum boasts a vast red steam engine that one might assume dated to the industrial revolution. In fact, it was built in the 20th century and powered a Lancashire cotton mill well into the space age.

As David Edgerton argues in his splendid book The Shock of the Old, we persistently conflate the technological frontier with the workhorse technologies we actually use. Long after the cutting edge of warfare seemed to be tanks and aeroplanes, horses remained a military essential. The German Wehrmacht began the second world war with more than 500,000 horses and doubled that number by 1945. Workhorse technologies indeed.

Old technologies remain relevant for several reasons. Sometimes they work as well as anything newer. For their intended purposes, bricks, condoms, forks, paper and shipping containers are all hard to beat. Old tools can long thrive alongside newer tricks.

Even once an approach seems thoroughly obsolete, it can be catapulted back to the technological frontier. Electric cars may be the vehicles of the future, but they are the vehicles of the past, too: the first land speed record, of nearly 40 miles an hour, was set in 1898 in an electric car.

The cutting edge can also be prohibitively expensive. Concorde, a plane of similar vintage to the Boeing 747, was a technological miracle — and a commercial catastrophe. Even inventions that are economically viable for some may not reach everyone. Professor Edgerton notes that cars, planes and television all spread rapidly around the world but not so rapidly from rich to poor. It may take a long time before today’s hot new thing is available to the typical citizen of the planet. Indeed, as with Concorde, that technological trickle-down may never happen.

A technology may also thrive because it offers advantages that the supposedly superior approach does not. A phone call is a lot cheaper and faster than a transatlantic flight. And while I ride a bike most days, that is not because I cannot afford a car: it is because in a city the bike is fun to ride, effortless to park and a much faster way to get around.

It seems that I am not alone. Globally, bicycle production has exceeded car production for decades. Yet thanks to pandemic-induced demand, there is now a worldwide shortage of bikes. If self-driving electric cars catch on, cyclists may flourish further on safer streets. Conscientious robots driving zero-emission vehicles beat erratic humans driving diesel cars.

One reason all this matters is that old technology adds inertia to our economic system. If we want to take action on climate change — and I sometimes despairingly wonder if that is true — then we must recognise how long it takes to change the old way of doing things.

The problem is sometimes described as “carbon lock-in”, as the typical house, or car, or power generator, falls far short of the cleanest, most efficient option. This is not just about clean energy: from vaccines to the internet, it can take too long for everyone to gain access to the benefits of innovation.

But in the scramble to get to what is new, we shouldn’t overlook the value of what is old. Many cities are so perfectly suited to bicycles, buses and trams that it is hard to understand quite why anyone let the cars in to them in the first place. Perhaps we fell so in love with what was new that we forgot to ask whether it was also the best technology we had.

So, as we say a long goodbye to the 747, let’s think hard about what we choose to replace it. No doubt that will partly be newer, fancier airliners. But let’s not forget the long-distance telephone call: 105 years old and still going strong.

Written for and first published in the Financial Times on 24 July 2020.

My NEW book The Next Fifty Things That Made the Modern Economy is out – and full of under-appreciated (and occasionally archaic) inventions.

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Rats, mazes, and the power of self-fulfilling prophecies

It’s 1963. A young psychologist named Bob Rosenthal conducts an experiment in which his assistants place rats in mazes, and then time how long it takes the rats to find the exit. They are housed in two cages: one for the smartest rats and one for rodent mediocrities. The assistants are not surprised to find that the smart rats solve the mazes more quickly.

Their supervisor is — because he knows that in truth, both cages contain ordinary lab rats. Prof Rosenthal — he would go on to chair Harvard’s psychology department — eventually concluded that the secret ingredient was the expectations of his assistants: they treated the “special” rats with care and handled the “stupid” rats with disdain. When we expect the best, we get the best — even if we expect it of a rat.

The story is well told in Rutger Bregman’s new book Humankind. His interest in Prof Rosenthal’s work is not hard to explain. Mr Bregman argues that people are fundamentally friendly and self-motivated. But he also argues that when we expect more of each other then, like the rats, we rise to the occasion. If schools, police or corporations believe that people are sluggish, dishonest or lazy, they may be proved right.

Prof Rosenthal coined the phrase “the Pygmalion effect”, the name inspired by Ovid’s account of a sculptor whose infatuation with a statue brings it to life. But the Pygmalion effect is just one example of what the sociologist Robert K. Merton called “self-fulfilling prophecies”.

There’s the placebo effect and its malign twin, the “nocebo effect”: if the doctor tells you a drug may produce side effects, some patients feel those side effects even if given an inert pill.

Self-fulfilling prophecies are a staple of economics. A recession can be caused by the expectation of a recession, if people hesitate to spend, hire or invest. And a bank run is the quintessential self-fulfilling prophecy.

The self-defeating prophecy is just as fascinating, and a problem that bedevils economic forecasters. If I credibly predict a surge in the price of oil next year, the surge will happen immediately as oil traders buy low now to sell high later. The forecast goes awry precisely because people thought it was accurate.

The coronavirus era has brought us a vivid example. A vocal minority argues that Covid-19 is not much worse than the influenza we ignore every winter, so both mandatory lockdowns and voluntary precautions have been unnecessary.

A glance at the data gives that argument a veneer of plausibility. The UK has suffered about 65,000 excess deaths during the first wave of the pandemic, and 25,000-30,000 excess deaths are attributed to flu in England alone during bad flu seasons. Is the disparity so great that the country needed to grind to a halt?

The flaw in the argument is clear: Covid was “only” twice as bad as a bad flu season because we took extreme measures to contain it. The effectiveness of the lockdown is being used as an argument that the lockdown was unnecessary. It is frustrating, but that is the nature of a self-defeating prophecy in a politicised environment.

One might say the same thing about Fort Knox. Nobody has ever tried to steal the gold, so why bother with all the guards?

Self-fulfilling prophecies can be pernicious; writing in 1948, Prof Merton focused on racism. For example, some said African Americans were strikebreakers who thus should not be allowed to join trade unions. Prof Merton pointed out that their exclusion from unions was the reason they had been strikebreakers. Sexism also drips with self-fulfilling prophecies. Since our leaders were usually straight white men in the past, it is all too easy to favour such people for leadership roles in the future.

Such prophecies can also be harnessed for good. Bob Rosenthal took his ideas into schools, where he found that what is true of rats being respectfully handled is just as true of pupils. Persuade a teacher that a pupil has hidden talents and the child will soon flourish.

Yet one can put too much faith in the self-fulfilling prophecy. Fervent excitement about the dizzy ascent of Tesla’s share price should help the company sell cars and raise funds but, in the long run, the value of a Tesla share will be determined by Tesla’s profitability.

Self-fulfilling prophecies are particularly tempting for politicians. It is all too easy to paint a project such as Brexit in Tinker Bell terms: if we clap our hands and believe in Brexit, it will not disappoint. Conveniently, all setbacks can be blamed on “Remoaners”.

The fact is that some things are false no matter how fervently we wish them to be true. We often plunge into projects with rosy views of how long they will take and how successful they will be, but our optimism only gets us started. It does not finish the job.

I think we should try to treat each other with kindness and respect, and not just because it worked for Bob Rosenthal’s rats. But there are limits to the power of sheer positive thinking. Even in the cartoons, Wile E. Coyote eventually feels the tug of gravity.

Written for and first published in the Financial Times on 17 July 2020.


My NEW book The Next Fifty Things That Made the Modern Economy is NOW OUT. Details, and to order on Hive, Blackwells, Amazon or Watersones. Bill Bryson comments, “Endlessly insightful and full of surprises — exactly what you would expect from Tim Harford.”



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How many buses to the dinosaur?

I had fun with Matt Parker – the world’s best Stand Up Mathematician – making this video about bad number analogies, and how to use “landmark numbers” to make the world add up.

To pre-order signed copies of “How To Make The World Add Up”, do please head over to MathsGear.