Types of International Lies

 

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The Israel Palestine Mis-Information war

 

Economics for Dummies 4: It's not the Figures Lying; but the Liars Figuring

 ChatGPT

The phrase "It's not the figures lying but the liars figuring" is a clever play on words that highlights the concept that deceptive or misleading information doesn't originate from the numbers themselves, but rather from the individuals who manipulate or interpret those numbers to suit their agenda. In other words, the problem isn't with the data itself, but with the people who present or analyze it dishonestly. Let's explore this idea further with several examples:

1. Political Manipulation: Imagine a politician using unemployment statistics to make a false claim about job growth during their term in office. They might present the figures in a way that only highlights a specific time frame or excludes certain groups from the calculation, making the situation seem better than it actually is. In this case, the figures themselves aren't lying; it's the politician who is manipulating the data to create a deceptive narrative.

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3. Marketing Deception: A company might advertise a product as "80% fat-free," emphasizing the low-fat aspect while conveniently ignoring that the product is loaded with sugar and unhealthy additives. The numeric figure (80%) isn't lying, but the company is deliberately omitting important information to mislead consumers about the overall healthiness of the product.

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5. Financial Misrepresentation: An investment advisor might use historical stock market data to convince potential clients that their investment strategy has consistently yielded high returns. However, they might conveniently leave out the years of losses or market crashes that occurred in between those successful periods. The data itself is accurate, but the omission of crucial information makes the overall representation deceptive.

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7. Media Manipulation: A news outlet could present crime statistics for a particular neighborhood, emphasizing a recent decrease in reported crimes. However, they might not mention that the police have changed their reporting methods, leading to a potential undercount of certain crimes. Here, the figures are accurate, but the media outlet is framing the information to create a misleading impression.

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9. Scientific Distortion: A study might be conducted on a new drug, and the researchers focus solely on the positive outcomes for a specific subgroup of participants while ignoring negative effects in a larger group. The statistics accurately reflect the results among the subgroup, but the study as a whole is presented in a way that distorts the overall effectiveness and safety of the drug.

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11. Historical Revisionism: A historian could present data on a historical event, emphasizing aspects that support a particular narrative while downplaying or ignoring contradictory evidence. This selective interpretation of historical figures and events can shape public understanding in a biased or misleading way.

In each of these examples, the underlying data or figures might be accurate, but it's the intentional manipulation, selective presentation, or omission of relevant information that leads to deception. The phrase "It's not the figures lying but the liars figuring" serves as a cautionary reminder to critically evaluate the context, interpretation, and motivations behind any presentation of information.

---Some more examples

1. Political Spin: During an election campaign, a candidate might boast about reducing the budget deficit by 50% during their tenure as mayor. While this figure is accurate, they conveniently omit the fact that the deficit was much higher when they took office, and their policies actually contributed to a slight increase in the deficit in recent years. The numbers themselves are true, but the candidate is shaping the narrative to make their performance seem more impressive than it is.

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3. Food Labeling Tricks: A cereal brand advertises that it contains "only 10g of sugar per serving," giving the impression of a healthy breakfast option. However, they fail to mention that the serving size is half of what an average person would eat, making the actual sugar content much higher. The figure presented is true, but it's manipulated to deceive consumers about the product's nutritional value.

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5. Stock Market Deception: A stockbroker promotes a trading strategy by highlighting a series of successful trades that generated substantial profits over a short period. What they don't disclose is that these successes were part of a high-risk gamble that wiped out most of their clients' investments in the long run. The actual trade figures are accurate, but the broker is manipulating the narrative to attract clients without revealing the full context.

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7. Cherry-Picked Research Findings: A pharmaceutical company publishes a study showing that their new medication has a higher success rate compared to a placebo. They omit the fact that the medication also has severe side effects in a significant number of cases. While the success rate data is true, the company is selectively presenting only the positive outcomes to create a favorable impression of the drug's effectiveness.

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9. Climate Change Denial: Critics of climate change might point to a period of unusually cold weather to argue that global warming is a hoax. They ignore the broader trend of rising global temperatures over decades, which is supported by extensive scientific data. While the localized cold weather figures are accurate, their selective use distorts the larger reality of climate change.

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11. Historical Manipulation: A country's government downplays the atrocities committed during a war, emphasizing instances where their military acted heroically while omitting documented cases of civilian casualties. This skewed presentation of historical figures and events seeks to shape a more favorable national narrative, despite the factual accuracy of the individual incidents mentioned.

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Britain is the Dorian Gray economy, hiding its ugly truths from the world. Now they are exposed

From Tony Blair to George Osborne, our rulers painted false pictures of success while real wealth and wages withered away writes Aditya Chakrabortty in The Guardian 

You know the central conceit of Oscar Wilde’s The Picture of Dorian Gray, of course you do. A lad of sun-kissed beauty is presented with a stunning likeness of himself. Disturbed at the notion that he will grow old while the painting doesn’t, he locks it away – where it is the portrait that ages and uglifies while Dorian stays boyish and beautiful. But perhaps you’ve forgotten what happens next.

The story has come to my mind many times, as the foulness of British politics becomes ever harder to ignore. Genteel liberals wonder how their land of cricket whites and orderly queues could be ruled by a grasping liar such as Boris Johnson and I hear a whisper on the wind: Dorian Gray. The New York Times and Der Spiegel report in bewilderment on a country with pockets of deep poverty and unslaked anger, and again rasps that hoarse voice: the horror was hidden here all along.

Now it’s all out in the open. In one of the richest societies in human history, inhabitants are starting to twig that by 2030 or thereabouts they will earn less per head than the Poles they so recently patronised. Whatever the politicians and pundits may argue, this debacle owes nothing to Jeremy Corbyn or Brexit or any supposedly un-British “populism”. It is homegrown and has deep roots.

Like Dorian Gray, Britain has for too long presented one face to the world while concealing the awful truth. The author of that novel, Oscar Wilde, was the son of an Irish nationalist and a graduate of Oxford, where he became a fine student of the British upper classes and their mellifluous hypocrisy. He would have recognised much of the mess we’re in, because it grew among shadows and cover-ups. From Tony Blair’s Cool Britannia through to George Osborne’s “march of the makers”, our rulers have trumpeted every false success, while ugly facts have been waved away as anomalies: from the former manufacturing suburbs and towns turned into giant warehouses of surplus people, to the fact that 15% of adults in England are on antidepressants. We’re winning the global race, claimed David Cameron, even as the population’s life expectancy fell far behind other rich countries. We shan’t stunt future generations with debt, he boasted, as our five-year-olds became the shortest in Europe.

Or take the housing bubble that politicians pretended was true prosperity – until this week, as the Bank of England hiked rates for the 13th time in a row and the prospect of it bursting began to terrify them. Yet the Westminster classes blew their hardest into that bubble. As soon as estate agents were out of lockdown, Rishi Sunak gave up £6bn of taxpayers’ money for a stamp duty holiday – an act as prudent as pouring petrol on a fire. Many of those he lured up the property ladder will be hardest hit by rising mortgage rates. Analysis done for me by UK Finance suggests that 465,000 house purchases during that tax break were financed with two- or three-year fixed rate mortgages – the very ones running out right now. In other words, nearly half a million households took the chancellor’s inducement; many will plunge into dangerous financial straits; some face losing their homes. They were mis-sold a dream by Sunak. Still, at least the Tories enjoyed a bounce in the polls.

Helmut Berger stars in the 1970 film adaptation of The Picture of Dorian Gray. Photograph: Sargon/Kobal/Rex/Shutterstock


“Sin is a thing that writes itself across a man’s face,” Dorian is told by his portraitist Basil Hallward. “If a wretched man has a vice, it shows itself in the lines of his mouth, the droop of his eyelids, the moulding of his hands even … But you, Dorian, with your pure, bright, innocent face and your marvellous untroubled youth – I can’t believe anything against you.” The picture of Dorian, which would have revealed the grotesque truth, is hidden away. So, too, has the UK avoided admitting its ills. Even now, in a country where patently so little works for people who rely on work for their income, commentators and frontbenchers still blame supposedly all-powerful interlopers: Boris, Nigel, Jeremy. And from Sunak to Starmer, all push growth and jobs as the remedy for what ails us.

Yet growth in this country is falling and not because of Ukraine or Covid or Brexit. Since the 1950s, the growth rate adjusted for inflation has been on a gentle but insistent downward slide. Our economy has become ever more stagnant and dependent on debt. It is fatuous to pretend this is going to turn around through magicking Britain into an AI free-for-all or a jolly green industrial giant. Employment? One in four employees are on low weekly wages – either because the pay is too low or the hours aren’t enough – while the average real wage has flatlined for many years.

Much of this analysis comes from a new book, When Nothing Works, written by a team of scholars. Although specialising in economics and accountancy, what they have produced is an essential text for understanding British government: the polarised politics of a highly unequal and increasingly stagnant society.

Take the issue at the top of today’s agenda: wages. Why can’t you and I take home more money? Because of a lack of productivity, politicians will say. Yet the researchers point to how labour has got a smaller and smaller share of economic output since the 1970s.

If the same share of GDP was paid out in wages today as in 1976, the average working-age household would have an extra £9,744 a year. We haven’t lost that 10 grand a year through laziness at work but because politicians from Thatcher onwards smashed up trade unions, undermined labour rights, and crowed over the result as a “flexible labour market”. What they really created was a low-wage workforce, in a low-growth country ruled by politicians with low ambitions for everyone bar themselves.

“The prayer of your pride has been answered,” Basil counsels Dorian, when he finally sees the portrait and its horrific truth. “The prayer of your repentance will be answered also.” When Nothing Works will inevitably be termed pessimistic, but it is no such thing. Realism comes from facing who we are and dropping the pretence that a growth miracle is just around the corner. Instead of trying to boost “the economy”, it is high time to boost our people: to ensure they have the basics they need to live a life free from indignity and free to flourish. This will come from redistribution rather than growth, from replacing extractive businesses with fair ones. Such ideas will not go down well in SW1, where both Tory and Labour are increasingly hostile to pluralism and brittle in their dogmatism. Self-knowledge is the hardest knowledge, as one of the book’s authors, Karel Williams, says. And self-delusion leads eventually to disaster.

Unable to face his loathsome self-image, Dorian slashes that portrait. He is found by servants. “Lying on the floor was a dead man, in evening dress, with a knife in his heart. He was withered, wrinkled and loathsome of visage. It was not till they examined the rings that they recognised who it was.”

Why the panic among Boris Johnson’s allies? Because they know Brexit is unravelling

There is an air of desperation in attacks from those on the right and their supporters in the press. They fear if Johnson falls, the Brexit deception will crumble too writes Michael Heseltine in The Guardian

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Did something change this month? Having proclaimed the Brexit referendum triumph of 2016 as the unique achievement of Boris Johnson and praised his historic success in the election three years later with the slogan “get Brexit done”, did the wreckers of the European dream slowly begin to realise that if Johnson goes, it shifts the sands from beneath their feet?

I’m the president of European Movement – Andrew Adonis is chair – and between us we agreed that this link needed a public airing. Learning from the direct and simple messaging of the anti-European newspapers, we felt the phrase: “If Boris goes, Brexit goes” said it clearly enough. Adonis duly tweeted it, to the horror of the pro-Brexit press.

The past few weeks have been a torrid time for the prime minister. He designed a set of restrictions he said were of critical importance for our safety and for the ability of the NHS to cope with the pandemic. He was right to do so. But disclosures since give the clearest impression that he not only broke the rules, but that he also misled parliament.

Johnson said he would accept the findings of Sue Gray’s inquiry, in stark contrast to his treatment of Sir Alex Allan’s report into the home secretary’s behaviour in 2020.

I believe he is entitled to insist that matters are not prejudged prior to the release of the full findings of the Gray inquiry, and the completion of the Metropolitan police investigation. I do not believe in the rule of the mob.

But a great deal hangs on this. If the prime minister is found to have lied to parliament and to the people, what defence is there to the allegation that the Brexit cause – mired in similar controversy over lies and dissembling – was conducted with the same disregard for the truth?

We all have a clear memory of the Brexit campaign and what was said. That we were being run by Brussels. That European restrictions were holding back our economy and lowering our living standards. That we could keep all the benefits of the single market and customs union, while negotiating trade deals with faster-growing countries in a world that was shifting east. That we had to regain control over our borders. That there would be no new border between Northern Ireland and mainland Great Britain, and that the Good Friday agreement, having ended years of strife, would be fully honoured

Theresa May became prime minister and immediately handed important offices of state to the three leading Brexiters. Boris Johnson went to the Foreign Office. David Davis went to the Department for Exiting the European Union, and Liam Fox to the Department for International Trade. They had their hands on the levers of power for two years before Johnson and Davis resigned, claiming their jobs were impossible.

Having ousted May, they claimed that a bare-bones trade deal – without most of the benefits of the customs union and the single market – was “oven ready” and would “get Brexit done”. In a straight contest with the unelectable Jeremy Corbyn, Johnson secured his mandate.

Except their deal didn’t “get Brexit done”. Within months it had seriously frustrated trade between Northern Ireland and Great Britain, and the government threatened to tear up the very deal it had itself negotiated to safeguard the position of Northern Ireland. Lord Frost resigned from the cabinet as Brexit minister last December after less than a year, complaining of the Covid strategy but also bemoaning that, regarding Brexit, the correct agenda was not being pursued.

Characteristically, he gave no detail as to what that agenda should have been or who was holding it up, but the villains were familiar: the metropolitan elite, the civil service, the BBC, Brussels, the remoaners – more or less anybody, and now including myself and Andrew Adonis. Everyone except the actual people in positions of power.

That is why February 2022 feels so significant. The cry has been growing louder. The right wing has been circling. Letters have been landing on the chairman of the 1922 committee’s desk. Something must be done. Reshuffle the pack, create a new government department and put yet another Brexiter in charge to pluck all those low-hanging plums that proved beyond the reach of predecessors.

Anyone with experience of Whitehall knows what happens next. The nameplates will change and the same civil servants will have new titles without actually moving their offices. But they will face exactly the same questions that have now been unanswered for five years. What is Brexit all about?

Jacob Rees-Mogg, Lord Frost’s spiritual successor in his new role as minister for Brexit opportunities, has a novel approach. He told the Sun last week that he is bypassing the civil service to ask if anyone else in the country has any ideas about Brexit benefits. Sun readers are invited to write to him with suggestions and he will see what can be done. But that too is revealing. One of the first tests officials apply to new ministers is to ask if they know what they want and to assess whether they have the ability to communicate that to them. I am afraid that Rees-Mogg has not passed this test, which is all the more surprising as he had plenty of time lounging on the government frontbench, listening to suggestions from Brexit-supporting Tory MPs.

So did something happen in February 2022? Maybe it’s just a feeling, a cloud no bigger than a man’s fist, the first breath of wind before the storm when the Daily Mail and the Daily Telegraph employ two of their most renowned columnists to attack Andrew Adonis and myself, merely for making the point that their hero may have feet of clay and take the Brexit house down with him. Perhaps they have smelled the wind, just as I have.

Boris Johnson is Britain's most honest politician

Bagehot in The Economist

 

Boris Johnson lies often and easily. It is the hallmark of his career. He was fired from his first job, at the Times, for fabricating a quote. As a condition of becoming editor of the Spectator he promised not to stand as an mp, and then promptly did just that. As a shadow minister, he was fired by Michael Howard for lying about an affair. (He later divorced after a few more.) While mayor of London, he said numerous times that he would not stand in the 2015 election, only to turn up as a candidate in Uxbridge. 

Lying about attending a garden party at Downing Street in May 2020, at the height of lockdown, is just the latest in a very long list. When public anger grew, mps protested with all the sincerity of Captain Renault entering a gambling den in Casablanca. Douglas Ross, a Scottish mp who voted for the prime minister in the Conservative leadership election, labelled the prime minister’s position “untenable” and demanded he quit. Why did such defenders of truth once back a man they knew to be an enthusiastic liar? Because Mr Johnson is, in his own way, a man of his word.

When he was drumming up support for his bid for party leader, his pitch was simple: back me, keep your seat, defeat Jeremy Corbyn and do Brexit. And it all came true. Mr Corbyn was crushed and the biggest Conservative majority in three decades followed. In that election Mr Johnson promised two big things and did both. The nhs would be showered with cash, which it has been. And he would do a deal with the eu, which he did.

It was not a good deal, but it was quick and it was clear. Coming after a negotiation with the eu that lacked both speed and simplicity, it is little surprise that voters jumped at it. Mr Johnson’s predecessor, Theresa May, had obfuscated, attempting legalistic contortions to avoid Brexit’s brutal simplicities. Labour’s Brexit position was, in the words of one shadow cabinet minister, “bollocks”. Mr Johnson’s deal hobbles British business for little or no gain, beyond a point of principle. But it is, no more and no less, what he said he would do.

Political lying was not invented by Mr Johnson in the Brexit campaign, comforting though that idea might be. Indeed, the misleading claims of the Leave campaign sometimes revealed awkward truths. When it pointed out that Turkey was in the long process of joining the eu, for example, Remainers cried foul because other countries were likely to block its accession. Yet David Cameron could have promised to veto Turkish membership of the eu, and did not. Turkey joining the club was a long-standing British policy.

In politics, integrity is almost inevitably followed by hypocrisy. Politicians with firm moral centres can crack. Gordon Brown was feted as a son of the manse while hurling handsets at people’s heads. Tony Blair runs an institute dedicated to openness while accepting money from despots. Sir Keir Starmer stood for Labour leader by pitching himself as Mr Corbyn in a suit, and then ditched the leftiest proposals once he had won. Mr Johnson, by contrast, does not even pretend to be a family man, despite having a few of them. He has not pretended to be anything but a power-hungry cynic either. A lack of integrity becomes a form of integrity.

A competent administrator never lurked beneath that mop of thinning hair. Occasionally, a journalist has claimed otherwise in a breathless profile; Mr Johnson has not. Those who work closely with him cannot say they were fooled into thinking he was a loyal boss. His time as prime minister has been marked by the defenestration of aides. When trouble strikes Mr Johnson, deputy heads roll. Being a civil servant rather than a political appointee offers no protection. Those who help him out, for example by chipping in for new curtains in Number 10 to keep his new wife happy, end up enmeshed in scandal.

No one can claim they were not warned about Mr Johnson. He is in no sense a mystery. He is the subject of several biographies and for the past three decades has shared his views about the world in newspaper columns and articles. If he is ever silenced by ministerial responsibility, a high-profile relative can fill the gap with more Johnson trivia. Throughout his career he has left a trail of giggling journalistic colleagues with a cherished Boris story to be whipped out on special occasions, no matter how long ago or dull. The content of his character was known and yet people still saw fit to put him in power.

If voters are souring on Mr Johnson, they only have themselves to blame. The prime minister is not a monarch. In 2019 he won 43.6% of the vote, the biggest share since Margaret Thatcher. Mr Johnson is in Downing Street because just under half the country ticked a box next to a Conservative’s name. Voters are adults. They knew what they were voting for, and they voted for what they got.

It is common to blame the rise of Mr Johnson on “Have I Got News For You”, a bbc1 news quiz on which he was a frequent guest. Ian Hislop, one of the team captains, has a tart reply: “If we ask someone on and people like them, that is up to people.” Mr Johnson is not a boil that can be lanced, at which point Britain’s body politic will recover. British politics, its systems and culture, deteriorated to the point where an honest liar proved attractive. Mr Johnson benefited from chaos created by others.

Small lies, big truths

Those mps who helped put Mr Johnson in power must now decide whether to sack him for sins he has never hidden. Their choice will be made by calculating whether their voters still want him. Popularity was all that he promised, and he delivered it—until now. If his rise is depressing, his potential fall offers a glimmer of hope. British voters have, at last, begun to grow tired of Mr Johnson’s record of honest lies. A less cynical politics may prosper and populism become unpopular. But optimism should be tempered. mps would not hesitate to keep Mr Johnson if he, in turn, helped them keep their seats. If those who put the prime minister in power bring him down, they do so to absolve themselves.

Richard Dawkins: No, Not All Opinions Are Equal—Elitism, Lies, and the Limits of Democracy

 

Shashi Tharoor discharged on murder charge - shame on the prosecution!

 

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

In an age of disinformation, the value of rigorous data has never been more evident writes Tim Harford in The FT 

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 weaponized 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. 

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. 

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. 

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. 

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. 

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. 

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.