I wrote a novel about my family. What could go wrong?

All writers are thieves but when it comes to stealing from your own flesh and blood — that way danger lies writes Francesca Jakobi in The Financial Times


In the wedding photograph my grandmother is not quite smiling. She is wearing white from top to toe — the only one to do so; the bride wore turquoise — and clutching a small glass of wine. 

The snapshot was taken on the day my parents married in 1964. Gerdi was the mother of the groom. It was a bright summer’s day and London was swinging, but my grandmother looks guarded and anxious. 

It must have been hard for her, surrounded by her ex-husband’s relatives. She had had an affair during the second world war and lost custody of my father as a consequence. Their relationship never fully recovered, though it was plain to see that she adored him. She rang him most nights in the middle of supper, throughout my childhood. We’d chorus: “I wonder who that could be?” 

I’ve always been fascinated by my German Jewish grandmother. She was someone I loved deeply but never quite understood. I’d grown up hearing one side of the story: that she was weak and selfish, and had paid a heavy price for it. I wanted to know what might have led to the decisions she made. How could a loving mother walk out on her son? 

I used the black-and-white photograph as the basis of a story, imagining the wedding day from her point of view. The voice I wrote in was feisty and spiky, a million miles away from my shy, awkward grandmother. But it felt good to examine things from her perspective. It felt like I was giving her a hearing. 

It was only months later, as it grew into a novel, that I started to worry I’d been reckless. What I’d written was fiction, yet the story behind it was real. I was scared that it might expose my family when my instinct is always to protect them. The dirty linen one mustn’t wash in public was strewn across 300 pages. 

All writers are thieves. They steal material wherever they can find it: a grumpy exchange overheard on the bus, the spotty shoulders of a long-ago boyfriend. But stealing stories from your nearest and dearest — that way danger lies. The road is littered with feuds and disinheritances. If you loved your family, why would you risk it? 

My shelves are packed with books by writers who have taken that gamble, from AA Milne to Andrea Levy, Hanif Kureishi and Isabel Allende. Some reimagined a relative’s life, others used their offspring as a springboard to a whole new world. There’s an emotional truth at the heart of these books that attracts me. 

I have always wanted to write a novel. I had my first go when I was nine. It was four sheets of paper sewn together with crooked stitches. The title: When the Dead Cock Crowed. I don’t recall that much about the plot (it had something to do with time travel and poultry) but I remember the excitement of filling the pages with words, my vice-like grip on the felt-tip pen as I wrote in giant capitals “THE END”. 

That was the feeling I sought to recapture aged 25, when I tried to write “chick-lit”. It was 1997 and Bridget Jones was all the rage. I’d just come back from teaching English in Turkey and was unsure what to do with my life. It seemed I’d found the answer as I tapped away on my Canon Starwriter. Research? Who needs it. Plot? Just keep writing. I was propelled by the arrogance of youth. 

I hit 50,000 words before I ran out of steam. When I read the manuscript back, I was horrified by what I’d produced. First drafts are supposed to be rough, but this one was truly a stinker. I thank God that Turkish Delight never made it to the bookshops. 

The experience taught me just how difficult it is to write a novel and that making characters and events sound plausible is harder still. If I was going to devote that time and effort again, it had to be for something I believed in. 

I wanted an authentic tale, one that I felt qualified to tell. It took me until my late thirties to find what I was looking for. 

Right from the start my novel Bitter was a murky mix of fact and fiction. The protagonist had my grandmother’s name and the same loveless childhood in Germany. She lived in the same smart Swiss Cottage flat I remembered from countless visits. Her favourite restaurant, Luigi’s off Finchley Road, was one I had been to with Gerdi. 

But as I began to write, I realised I knew very little beyond the headline facts. My grandmother had been dead for almost a decade. In life, she rarely talked about the past and my father had been tucked away at boarding school. 

At first it felt strange to be making things up — it reminded me of playing with Barbie dolls — but the more imaginative leaps I made, the more natural it became. I wrote instinctively, mixing anecdotes with half-truths. I changed the protagonist’s name to Gilda (it had to have the same feeling as Gerdi) and that one small change was like cutting a tether; she took on a life of her own. 

Ambition is a peculiar thing: mine seemed to grow along with my word count. When I finally got to the end of the first draft, I thought I had something that could work. But along with that came my first serious doubts. I’d distorted the facts beyond recognition. My protagonist was an unlikeable woman with a life spinning out of control. Yet aspects of the story still belonged to my grandmother. I shuddered to think what she’d have made of it. 

I wasn’t the only one struggling to distinguish the truth. Shortly after I’d shown her the first draft, my mother recounted an anecdote about Gerdi and I realised it had come from the book. I told her I had made the story up, but she insisted it had actually happened. Perhaps it did. Perhaps I’d heard it at some point. Neither of us has any way of knowing. 

I returned to the manuscript and deleted several sections of it. I wrote the word “compassion” on a Post-it note and stuck it to my screen. The second draft was kinder, the characters more nuanced, the ending more hopeful. I added another layer of plot to push it further into fiction. 

As I set about the long process of trying to find an agent, I wondered whether to mention the family link. In the end I did. I wanted to show why I was the right person to write this particular story. And also, if I’m honest, I hoped it might be a selling point. 

When Lionel Shriver wrote her fifth novel, A Perfectly Good Family, she thought it might cause “a little aggro”. In fact, her brother refused to speak to her for two years. Writing more than a decade after the book’s publication, she warned: “Anyone considering writing fiction or a memoir that brushes even slightly against real-life family should take heed: think twice.” 

This is good advice, clearly. Even the most sensitive writers can cause unintended harm. AA Milne’s son Christopher was badly bullied at boarding school for his role in Winnie-the-Pooh. Isabel Allende’s relatives didn’t speak to her for several years after recognising themselves in her debut House of the Spirits. 

Some authors see such repercussions as part of the job description. Hanif Kureishi, whose 2003 film The Mother caused a serious rift with his sister, says his only regrets “are to do with quality”. Rachel Cusk, who was vilified for writing about motherhood and the breakdown of her marriage, has said “If you really care what people think of you . . . you’re never going to be a writer.” 

Yet most of the authors I know agonise about the possibility of hurting loved ones. A friend scrapped an entire manuscript because she was worried what her children would one day make of it. Another changed a crucial death scene because it was too close to what had happened to a relative. 

Andrea Levy shows that family stories need not be a source of conflict. Her novel Small Island came out of her father’s emigration to Britain from Jamaica on the Empire Windrush and her mother’s arrival six months later. The two main characters, Gilbert and Hortense, are named after her parents. In a piece for The Guardian, she said: “Small Island was a joy to write and those characters will stay with me forever. It became a work of fiction, but for me it still remains something of a family history too.” 

I asked my parents’ permission to write about Gerdi early on and both were supportive. When I speak to my mother now, she says she wasn’t worried at all. As a retired psychoanalyst, she knows the importance of telling stories. She trusted that I was writing from a place of love. I wasn’t trying to settle any scores. 

In fact, she rather wished that I was writing about her parents. She saw it as a way to somehow bring them back to life. I understand that. For a while it did feel like Gerdi was more present. I thought about her a great deal. We talked about her often at the dinner table. 

My father, it seemed to me, was not hugely interested. His childhood years were unhappy and he had no wish to return to them. But, aged 80, he had just completed a Masters in creative writing and I thought he understood where I was coming from. I spoke to him a bit about his mum. Neither of us expected my novel would be published. I showed him some passages along the way but he didn’t want to read it until it was a “proper book”. 

When I spoke to him for this article, he admits he’d had concerns about what I was doing. “I knew you didn’t know enough to write a decent memoir and I was worried you were going down the Hilary Mantel ‘faction’ route. I didn’t like the idea of you making things up to fill the gaps.”

Could I have written Bitter without my parents’ permission? Honestly, I don’t think so. It wasn’t just my grandmother’s archives I raided. The book is stuffed with family memories: my mother’s school dinners in the 1950s, my brother as a child learning chess with my dad, my nephew running as fast as he can through the autumn leaves, me hobbling across the stones to paddle in the sea at Brighton. 

I was nervous when I finally handed Dad a hardback copy. At first he said he was enjoying it. But Mum told me later that he was finding it quite upsetting. She thought chapters that touched on his early life had reminded him of a time he would rather forget. 

I rang him and he said it had captured something about his mother. It wasn’t Gerdi and yet somehow it was her — not the words, perhaps, but the underlying sadness. It was unsettling to see this period through the eyes of his daughter. I said he should stop reading it and he has. 

He’s since explained that he could never quite see it as fiction. To him Gilda is an imposter, pretending to be his mum. When he heard the actress in the audio version had a German accent, his response was immediate. “No. That’s wrong. Mum lost most of her accent.” 

I don’t doubt how proud he is, though. He took me out to lunch on publication day. As I got up to go to the ladies room, I saw him lean over to the strangers at the table next to us. Pointing in my direction, he said: “That’s my clever daughter.” 

Both my parents came to the launch party and my father thoroughly enjoyed himself. One of my friends mentioned a scene from the book that revolved around a small boy and some coffee cups. Dad told her: “That was me, you know.”

A simple guide to statistics in the age of deception

Tim Harford in The Financial Times





“The best financial advice for most people would fit on an index card.” That’s the gist of an offhand comment in 2013 by Harold Pollack, a professor at the University of Chicago. Pollack’s bluff was duly called, and he quickly rushed off to find an index card and scribble some bullet points — with respectable results. 

When I heard about Pollack’s notion — he elaborated upon it in a 2016 book — I asked myself: would this work for statistics, too? There are some obvious parallels. In each case, common sense goes a surprisingly long way; in each case, dizzying numbers and impenetrable jargon loom; in each case, there are stubborn technical details that matter; and, in each case, there are people with a sharp incentive to lead us astray. 

The case for everyday practical numeracy has never been more urgent. Statistical claims fill our newspapers and social media feeds, unfiltered by expert judgment and often designed as a political weapon. We do not necessarily trust the experts — or more precisely, we may have our own distinctive view of who counts as an expert and who does not.  

Nor are we passive consumers of statistical propaganda; we are the medium through which the propaganda spreads. We are arbiters of what others will see: what we retweet, like or share online determines whether a claim goes viral or vanishes. If we fall for lies, we become unwittingly complicit in deceiving others. On the bright side, we have more tools than ever to help weigh up what we see before we share it — if we are able and willing to use them. 

In the hope that someone might use it, I set out to write my own postcard-sized citizens’ guide to statistics. Here’s what I learnt. 

Professor Pollack’s index card includes advice such as “Save 20 per cent of your money” and “Pay your credit card in full every month”. The author Michael Pollan offers dietary advice in even pithier form: “Eat Food. Not Too Much. Mostly Plants.” Quite so, but I still want a cheeseburger.  

However good the advice Pollack and Pollan offer, it’s not always easy to take. The problem is not necessarily ignorance. Few people think that Coca-Cola is a healthy drink, or believe that credit cards let you borrow cheaply. Yet many of us fall into some form of temptation or other. That is partly because slick marketers are focused on selling us high-fructose corn syrup and easy credit. And it is partly because we are human beings with human frailties. 

With this in mind, my statistical postcard begins with advice about emotion rather than logic. When you encounter a new statistical claim, observe your feelings. Yes, it sounds like a line from Star Wars, but we rarely believe anything because we’re compelled to do so by pure deduction or irrefutable evidence. We have feelings about many of the claims we might read — anything from “inequality is rising” to “chocolate prevents dementia”. If we don’t notice and pay attention to those feelings, we’re off to a shaky start. 

What sort of feelings? Defensiveness. Triumphalism. Righteous anger. Evangelical fervour. Or, when it comes to chocolate and dementia, relief. It’s fine to have an emotional response to a chart or shocking statistic — but we should not ignore that emotion, or be led astray by it. 

There are certain claims that we rush to tell the world, others that we use to rally like-minded people, still others we refuse to believe. Our belief or disbelief in these claims is part of who we feel we are. “We all process information consistent with our tribe,” says Dan Kahan, professor of law and psychology at Yale University. 

In 2005, Charles Taber and Milton Lodge, political scientists at Stony Brook University, New York, conducted experiments in which subjects were invited to study arguments around hot political issues. Subjects showed a clear confirmation bias: they sought out testimony from like-minded organisations. For example, subjects who opposed gun control would tend to start by reading the views of the National Rifle Association. Subjects also showed a disconfirmation bias: when the researchers presented them with certain arguments and invited comment, the subjects would quickly accept arguments with which they agreed, but devote considerable effort to disparage opposing arguments.  

Expertise is no defence against this emotional reaction; in fact, Taber and Lodge found that better-informed experimental subjects showed stronger biases. The more they knew, the more cognitive weapons they could aim at their opponents. “So convenient a thing it is to be a reasonable creature,” commented Benjamin Franklin, “since it enables one to find or make a reason for everything one has a mind to do.” 

This is why it’s important to face up to our feelings before we even begin to process a statistical claim. If we don’t at least acknowledge that we may be bringing some emotional baggage along with us, we have little chance of discerning what’s true. As the physicist Richard Feynman once commented, “You must not fool yourself — and you are the easiest person to fool.” 

The second crucial piece of advice is to understand the claim. That seems obvious. But all too often we leap to disbelieve or believe (and repeat) a claim without pausing to ask whether we really understand what the claim is. To quote Douglas Adams’s philosophical supercomputer, Deep Thought, “Once you know what the question actually is, you’ll know what the answer means.” 

For example, take the widely accepted claim that “inequality is rising”. It seems uncontroversial, and urgent. But what does it mean? Racial inequality? Gender inequality? Inequality of opportunity, of consumption, of education attainment, of wealth? Within countries or across the globe? 

Even given a narrower claim, “inequality of income before taxes is rising” (and you should be asking yourself, since when?), there are several different ways to measure this. One approach is to compare the income of people at the 90th percentile and the 10th percentile, but that tells us nothing about the super-rich, nor the ordinary people in the middle. An alternative is to examine the income share of the top 1 per cent — but this approach has the opposite weakness, telling us nothing about how the poorest fare relative to the majority.  

There is no single right answer — nor should we assume that all the measures tell a similar story. In fact, there are many true statements that one can make about inequality. It may be worth figuring out which one is being made before retweeting it. 

Perhaps it is not surprising that a concept such as inequality turns out to have hidden depths. But the same holds true of more tangible subjects, such as “a nurse”. Are midwives nurses? Health visitors? Should two nurses working half-time count as one nurse? Claims over the staffing of the UK’s National Health Service have turned on such details. 

All this can seem like pedantry — or worse, a cynical attempt to muddy the waters and suggest that you can prove anything with statistics. But there is little point in trying to evaluate whether a claim is true if one is unclear what the claim even means. 

Imagine a study showing that kids who play violent video games are more likely to be violent in reality. Rebecca Goldin, a mathematician and director of the statistical literacy project STATS, points out that we should ask questions about concepts such as “play”, “violent video games” and “violent in reality”. Is Space Invaders a violent game? It involves shooting things, after all. And are we measuring a response to a questionnaire after 20 minutes’ play in a laboratory, or murderous tendencies in people who play 30 hours a week? “Many studies won’t measure violence,” says Goldin. “They’ll measure something else such as aggressive behaviour.” Just like “inequality” or “nurse”, these seemingly common sense words hide a lot of wiggle room. 

Two particular obstacles to our understanding are worth exploring in a little more detail. One is the question of causation. “Taller children have a higher reading age,” goes the headline. This may summarise the results of a careful study about nutrition and cognition. Or it may simply reflect the obvious point that eight-year-olds read better than four-year-olds — and are taller. Causation is philosophically and technically a knotty business but, for the casual consumer of statistics, the question is not so complicated: just ask whether a causal claim is being made, and whether it might be justified. 

Returning to this study about violence and video games, we should ask: is this a causal relationship, tested in experimental conditions? Or is this a broad correlation, perhaps because the kind of thing that leads kids to violence also leads kids to violent video games? Without clarity on this point, we don’t really have anything but an empty headline.  

We should never forget, either, that all statistics are a summary of a more complicated truth. For example, what’s happening to wages? With tens of millions of wage packets being paid every month, we can only ever summarise — but which summary? The average wage can be skewed by a small number of fat cats. The median wage tells us about the centre of the distribution but ignores everything else. 

Or we might look at the median increase in wages, which isn’t the same thing as the increase in the median wage — not at all. In a situation where the lowest and highest wages are increasing while the middle sags, it’s quite possible for the median pay rise to be healthy while median pay falls.  

Sir Andrew Dilnot, former chair of the UK Statistics Authority, warns that an average can never convey the whole of a complex story. “It’s like trying to see what’s in a room by peering through the keyhole,” he tells me.  

In short, “you need to ask yourself what’s being left out,” says Mona Chalabi, data editor for The Guardian US. That applies to the obvious tricks, such as a vertical axis that’s been truncated to make small changes look big. But it also applies to the less obvious stuff — for example, why does a graph comparing the wages of African-Americans with those of white people not also include data on Hispanic or Asian-Americans? There is no shame in leaving something out. No chart, table or tweet can contain everything. But what is missing can matter. 

Channel the spirit of film noir: get the backstory. Of all the statistical claims in the world, this particular stat fatale appeared in your newspaper or social media feed, dressed to impress. Why? Where did it come from? Why are you seeing it?  

Sometimes the answer is little short of a conspiracy: a PR company wanted to sell ice cream, so paid a penny-ante academic to put together the “equation for the perfect summer afternoon”, pushed out a press release on a quiet news day, and won attention in a media environment hungry for clicks. Or a political donor slung a couple of million dollars at an ideologically sympathetic think-tank in the hope of manufacturing some talking points. 

Just as often, the answer is innocent but unedifying: publication bias. A study confirming what we already knew — smoking causes cancer — is unlikely to make news. But a study with a surprising result — maybe smoking doesn’t cause cancer after all — is worth a headline. The new study may have been rigorously conducted but is probably wrong: one must weigh it up against decades of contrary evidence. 

Publication bias is a big problem in academia. The surprising results get published, the follow-up studies finding no effect tend to appear in lesser journals if they appear at all. It is an even bigger problem in the media — and perhaps bigger yet in social media. Increasingly, we see a statistical claim because people like us thought it was worth a Like on Facebook. 

David Spiegelhalter, president of the Royal Statistical Society, proposes what he calls the “Groucho principle”. Groucho Marx famously resigned from a club — if they’d accept him as a member, he reasoned, it couldn’t be much of a club. Spiegelhalter feels the same about many statistical claims that reach the headlines or the social media feed. He explains, “If it’s surprising or counter-intuitive enough to have been drawn to my attention, it is probably wrong.”  

OK. You’ve noted your own emotions, checked the backstory and understood the claim being made. Now you need to put things in perspective. A few months ago, a horrified citizen asked me on Twitter whether it could be true that in the UK, seven million disposable coffee cups were thrown away every day.  

I didn’t have an answer. (A quick internet search reveals countless repetitions of the claim, but no obvious source.) But I did have an alternative question: is that a big number? The population of the UK is 65 million. If one person in 10 used a disposable cup each day, that would do the job.  

Many numbers mean little until we can compare them with a more familiar quantity. It is much more informative to know how many coffee cups a typical person discards than to know how many are thrown away by an entire country. And more useful still to know whether the cups are recycled (usually not, alas) or what proportion of the country’s waste stream is disposable coffee cups (not much, is my guess, but I may be wrong).  

So we should ask: how big is the number compared with other things I might intuitively understand? How big is it compared with last year, or five years ago, or 30? It’s worth a look at the historical trend, if the data are available.  

Finally, beware “statistical significance”. There are various technical objections to the term, some of which are important. But the simplest point to appreciate is that a number can be “statistically significant” while being of no practical importance. Particularly in the age of big data, it’s possible for an effect to clear this technical hurdle of statistical significance while being tiny. 

One study was able to demonstrate that unborn children exposed to a heatwave while in the womb went on to earn less as adults. The finding was statistically significant. But the impact was trivial: $30 in lost income per year. Just because a finding is statistically robust does not mean it matters; the word “significance” obscures that. 

In an age of computer-generated images of data clouds, some of the most charming data visualisations are hand-drawn doodles by the likes of Mona Chalabi and the cartoonist Randall Munroe. But there is more to these pictures than charm: Chalabi uses the wobble of her pen to remind us that most statistics have a margin of error. A computer plot can confer the illusion of precision on what may be a highly uncertain situation. 

“It is better to be vaguely right than exactly wrong,” wrote Carveth Read in Logic (1898), and excessive precision can lead people astray. On the eve of the US presidential election in 2016, the political forecasting website FiveThirtyEight gave Donald Trump a 28.6 per cent chance of winning. In some ways that is impressive, because other forecasting models gave Trump barely any chance at all. But how could anyone justify the decimal point on such a forecast? No wonder many people missed the basic message, which was that Trump had a decent shot. “One in four” would have been a much more intuitive guide to the vagaries of forecasting.

Exaggerated precision has another cost: it makes numbers needlessly cumbersome to remember and to handle. So, embrace imprecision. The budget of the NHS in the UK is about £10bn a month. The national income of the United States is about $20tn a year. One can be much more precise about these things, but carrying the approximate numbers around in my head lets me judge pretty quickly when — say — a £50m spending boost or a $20bn tax cut is noteworthy, or a rounding error. 

My favourite rule of thumb is that since there are 65 million people in the UK and people tend to live a bit longer than 65, the size of a typical cohort — everyone retiring or leaving school in a given year — will be nearly a million people. Yes, it’s a rough estimate — but vaguely right is often good enough. 

Be curious. Curiosity is bad for cats, but good for stats. Curiosity is a cardinal virtue because it encourages us to work a little harder to understand what we are being told, and to enjoy the surprises along the way.  

This is partly because almost any statistical statement raises questions: who claims this? Why? What does this number mean? What’s missing? We have to be willing — in the words of UK statistical regulator Ed Humpherson — to “go another click”. If a statistic is worth sharing, isn’t it worth understanding first? The digital age is full of informational snares — but it also makes it easier to look a little deeper before our minds snap shut on an answer.  

While curiosity gives us the motivation to ask another question or go another click, it gives us something else, too: a willingness to change our minds. For many of the statistical claims that matter, we have already reached a conclusion. We already know what our tribe of right-thinking people believe about Brexit, gun control, vaccinations, climate change, inequality or nationalisation — and so it is natural to interpret any statistical claim as either a banner to wave, or a threat to avoid.  

Curiosity can put us into a better frame of mind to engage with statistical surprises. If we treat them as mysteries to be resolved, we are more likely to spot statistical foul play, but we are also more open-minded when faced with rigorous new evidence. 

In research with Asheley Landrum, Katie Carpenter, Laura Helft and Kathleen Hall Jamieson, Dan Kahan has discovered that people who are intrinsically curious about science — they exist across the political spectrum — tend to be less polarised in their response to questions about politically sensitive topics. We need to treat surprises as a mystery rather than a threat.  

Isaac Asimov is thought to have said, “The most exciting phrase in science isn’t ‘Eureka!’, but ‘That’s funny…’” The quip points to an important truth: if we treat the open question as more interesting than the neat answer, we’re on the road to becoming wiser.  

In the end, my postcard has 50-ish words and six commandments. Simple enough, I hope, for someone who is willing to make an honest effort to evaluate — even briefly — the statistical claims that appear in front of them. That willingness, I fear, is what is most in question.  

“Hey, Bill, Bill, am I gonna check every statistic?” said Donald Trump, then presidential candidate, when challenged by Bill O’Reilly about a grotesque lie that he had retweeted about African-Americans and homicides. And Trump had a point — sort of. He should, of course, have got someone to check a statistic before lending his megaphone to a false and racist claim. We all know by now that he simply does not care. 

But Trump’s excuse will have struck a chord with many, even those who are aghast at his contempt for accuracy (and much else). He recognised that we are all human. We don’t check everything; we can’t. Even if we had all the technical expertise in the world, there is no way that we would have the time. 

My aim is more modest. I want to encourage us all to make the effort a little more often: to be open-minded rather than defensive; to ask simple questions about what things mean, where they come from and whether they would matter if they were true. And, above all, to show enough curiosity about the world to want to know the answers to some of these questions — not to win arguments, but because the world is a fascinating place. 

The myth of post-truth

The assumption is that my truth is as good as your truth, and hence all truths are immaterial and irrelevant. Such extreme relativism is a problem


Tabish Khair in The Hindu

It has been remarked that ‘post-truth’ is very different from similar terms with the prefix post-, such as postcolonialism and postmodernism. No one who uses postcolonialism or postmodernism argues that colonialism and modernism are no longer relevant. However, the assumption behind ‘post-truth’ is that the concept of truth is no longer relevant.

Why is there no post-falsehood?

The philosophical (or, in my view, anti-philosophical) aspects of ‘post-truth’ cannot be covered in a column — they would require a voluminous thesis. However, it is worth asking: why do we not talk of ‘post-falsehood’? After all, the opposite of truth is not post-truth but falsehood. In that case, if we can have an age of post-truth, we should be able to talk of an age of post-falsehood too. Having gone past truth, we should also be able to go past its opposite: falsehood. This, however, is not the case.

Partly, this has to do with the nature of truth and how we have understood it across cultures. Truth is seen as singular and fixed: it is generally felt that there can be only one truth, while there may be many falsehoods. Hence, we feel that to go past truth is to go past a singularity, but to go past falsehood might well mean to choose among multiple falsehoods.

There is another reason why ‘post-falsehood’ does not exist: strangely enough, it would come to mean ‘truth’. We instinctively feel that to go beyond generic falsehood is also to reach truth. That is because the positivity of truth cannot exist without the negativity of falsehood. The essential lie of ‘post-truth’ is exactly this: it is supposed not to suggest falsehood. But if there is no falsehood on the other side of truth, then there is no truth either. ‘Post-truth’ dismisses the very possibility of truth — and, by that act, it dismisses the existence of falsehood.

In short, it dismisses critical and scientific thinking, which are based not on eternal truth, which is religion’s penchant, but on a methodical and endless elimination of falsehoods. This is essentially what Karl Popper meant when he stressed that a scientific statement needs to be falsifiable.

It is nevertheless interesting to stand the matter on its head and pose this question: if we cannot talk of ‘post-falsehood’, surely the fact that we are talking of ‘post-truth’ means that there is actually a difference between truth and falsehood? And if that is the case, then, by definition, we can never have an age of ‘post-truth’ — in the sense of equating truths and falsehoods.

Truths are contextual

On the other hand, belief in a singular, unchanging truth is also what has led to the mistaken notion of an age of ‘post-truth’. That is so because the idea of one eternally fixed truth has been radically shaken over the past few centuries in different ways, most of which do not lead to extreme relativism but instead to a kind of contextualisation. However, this necessary shaking of given and fixed truths can be and is often converted into an extreme relativism by the loudly ignorant — a relativism in which all truths seem relative to you as an observer, and not to the complex context of the observation. This slippage inevitably leads to talk of post-truth, especially in fields outside the hard sciences.

In fact, truths are contextual — not relativist — in hard science too: the ‘truth’ of subatomic particles exists in the context of atoms, and the ‘truth’ of planetary systems in our universe exists in that context. These are not necessarily exclusive contexts, but only a seriously confused student would expect the rules that obtain within an atom to be the same as the rules that apply to our planetary system. This is what I mean by contextualisation.

Relativism, on the other hand, or at least extreme relativism (for many versions of what is called ‘relativism’ are basically contextualisation), extracts the observer from the context and makes the observer’s version paramount.

This is what lies at the core of ‘post-truth.’ The assumption is that my truth is as good as your truth, and hence all truths are immaterial and irrelevant. Need I note the problem of such extreme relativism, for it puts the observer outside a context, a context that can be and should be used to determine the ‘truth’ of his or her observations. Truths might not be eternally fixed, but we do get closer to what is true by comparing and contrasting our versions of it: to you it might be superman, to me it is a bird, but enough and better sightings will ascertain that it is actually a plane.

Hence, while one can argue about the details of evolution, the fact that both human beings and apes evolved from a common ancestor is more true than the claim that human beings were directly handcrafted by a god. There is overwhelming evidence of the former, and it can be dismissed only by stubborn acts of belief (or disbelief).
However, one should not oppose the myth of post-truth by returning to older and faulty myths of fixed, eternal truths. This too would block the necessary and fledgling project of critical inquiry. We need to maintain a balance between the dismissal of the difference between truth and falsehood and blind acceptance of given truths. The future of humanity depends on our precarious ability to maintain this delicate balance.

Tea and sympathy won't suffice as England face up to another drubbing

George Dobell in Cricinfo


There's a pattern of behaviour prevalent in England which dictates that, in times of extreme stress or emotion, we should do almost anything but acknowledge the truth.

So we sit around the hospital beds of the dying, telling them they'll soon be back on their feet. We tell doctors we hardly drink, never smoke and go the gym almost every night. We go to funerals and tell each other the wife-beating alcoholic had a heart of gold. Her bottom never looks big in that and there's almost nothing - not nuclear war or zombie apocalypse - that can't be overcome with a nice cup of tea.

It is, in some ways, a wonderful quality. It was that stoic refusal to acknowledge reality that enabled a previous generation to win a war that, in cricket terms, had them following on in gloomy light and on a pitch showing signs of uneven bounce. And the band on Titanic - just like the Barmy Army - played all the way down.

But there are moments when it is also an incredibly irritating characteristic. And damaging. So, just as you really should get that mole checked out, just as that lump probably won't go away, England really should acknowledge that this Ashes series really wasn't close.

There were moments - flashes might be a better word - when it looked as if England could compete. When James Vince reached 83 in Brisbane; when Australia were reduced to 76 for 4 in the same match; when Jonny Bairstow and Dawid Malan took England to 368 for 4 in Perth. On these occasions, it appeared England were working their way into a good position.

But they only made 302 in that first innings in Brisbane. They trailed by 215 on first innings in Adelaide (even though Australia declared their own first innings with eight wickets down). Only three men passed 25 in England's first innings in Perth, and only two men in the top seven managed more than 22 on the flattest Melbourne pitch you ever will wish you hadn't seen.

This was a team trying to snatch a goal on the break. This was Frank Bruno catching Mike Tyson with his left hook; Greg Thomas dislodging Viv Richards' cap; England's openers enjoying a good start (they were 101 without loss) against West Indies at Lord's in 1984; Graham Dilley reducing them to 54 for 5 at Lord's in 1988. Looking back now, they were far from reflective of the general balance of power. They were the cat hissing at the dog; the condemned man cursing his firing squad. To suggest they represent squandered opportunities is largely delusional.

So, while it's true that Steve Smith was a difference between the teams, he wasn't the only difference. The same could equally be said about Nathan Lyon and the Australian pace attack. So that's the batting, pace bowling and spin bowling covered, then. England were out-gunned from the start. They haven't squandered moments of great promise. They've occasionally caught sight of them in the distance when the clouds parted for a moment. But, actually, now they look again, it may have been a cow.

You can't really blame players for buying into the narrative - a narrative repeated several times by Joe Root and most recently by James Anderson - that the series was decided by a few key moments. It comes with the territory in top-level sport that the protagonists have to maintain high levels of self-belief. They have to believe they can win. It's part of the make-up of a champion.

But you would hope that none of those in positions of power fall for such nonsense. You would hope they reflect on this Ashes series - a series in which Australia scored in excess of 600 twice, won by an innings twice (despite losing the toss on both occasions), had the three highest run-scorers and four highest wicket-takers - and understand that it was a rout.

Nor should it be dismissed as an aberration. England have now lost nine of their most recent 11 overseas Tests. Sure, playing in Australia and India is tough. But England didn't win in the Caribbean, either. Or Bangladesh. Or New Zealand, the UAE or Sri Lanka. Living off their success against South Africa in 2015 - excellent result though it was - is a car driving on fumes.

It'll keep happening, too. Sure, they may snatch the odd series - perhaps in New Zealand in a couple of months, perhaps in the Caribbean at the start of 2019 - because they have, in Ben Stokes and Root and Anderson, a few top-quality players. But generally, such wins will come very much against the norm while England prioritise their white-ball development at the expense of their red-ball team. Until they can develop more spin and fast bowlers, until they stop hiding behind wins on home surfaces, they will remain also-rans in Test cricket.

Some will say this tour went wrong in September. And it is true England lost a key player - and just a bit of their energy and equilibrium - when Stokes was arrested that night in Bristol. Whatever the rights and wrongs of the affair (and the proper authorities can decide that) there are lessons to be learned about the level of sacrifice inherent in the life of an international sportsperson. There might well be some justification for some of Stokes' actions that night. But should he have been there in the first place?

But it went wrong long before that. It went wrong when the ECB continued their exclusive relationship with a subscription broadcaster long after it had become clear it was damaging the long-term health of the game. As a result, cricket lost relevance in the public consciousness. The talent pool on which the game relies has grown shallow and is absurdly over-reliant upon the private schools, Asian and ex-pat communities.

It went wrong when the Championship was shoved into the margins of the season, when counties were incentivised for fielding teams of young, England-qualified players, when the ECB stopped believing in their own domestic competitions and allowed them to be diluted and devalued.

While the suspicion lingers that Root caught the bug that laid him low on the final day of the series while eating jelly and ice-cream at a kid's birthday party (it was his son's birthday on the fourth day of the game), that will do nothing to derail the narrative that he lacks the maturity or gravitas of a leader, even though there is no evidence for that save his boyish face.

To see Root in the field, coaxing and cajoling his side into another effort, was to see a born leader. To see him behind the scenes, handling each crisis with calm good humour and ensuring this tour did not sink to the levels of the 2013-14 debacle, was to see a young man with strength, energy and integrity. He simply wasn't dealt a handful of aces. He's not the problem here.

And nor is Trevor Bayliss. Sure, he's not a technical coach. And nor is he a selector in the sense that he has the knowledge of county cricket to offer much there. His job, in essence, is to keep the first-team environment positive and focussed. And he's good at that. It's not his fault that England can't produce pace or spin bowlers. He's not an alchemist.

No, the trouble is much higher up the pyramid than that. The problem is the ECB chief executive, Tom Harrison, trying to kid us that English cricket is in good health, and Andrew Strauss who has achieved little in his time as director of England cricket other than settling a couple of old scores: getting rid of Peter Moores and Kevin Pietersen. If teams are judged by their success in global events - as Strauss has always said - it is worth remembering they did worse in the 2017 Champions Trophy than the 2013 Champions Trophy.

Blaming Stokes or Bayliss or Root for this loss will solve nothing. It's more fundamental change - and an acknowledgement of their problems - that England require. And a nice cup of tea. Obviously.

On Being a Hindu

Makarand Paranjpe 1

Makarand Paranjape 2

Hindol Sengupta

Facts do not matter

Amit Varma in The Hindu

The most surprising thing about these Gujarat elections is that people are so surprised at the Prime Minister’s rhetoric. Narendra Modi has eschewed all talk of development, and has played to the worst impulses of the Gujarati people. His main tool is Hindu-Muslim polarisation, which is reflected in the language he uses for his opponents. The Congress has a “Mughlai” mentality, they are ushering in an “Aurangzeb Raj”, and their top leaders are conspiring with Pakistan to make sure Mr. Modi loses. A Bharatiya Janata Party (BJP) spokesperson has also launched a scathing attack on Congress president-elect Rahul Gandhi. None of this is new.

Mr. Modi’s rhetoric in the heat of campaigning has always come from below. From his references to “Mian Musharraf” over a decade ago to the “kabristan-shamshaan” comments of the recent elections in Uttar Pradesh, it has been clear that the otherness of Muslims is central to the BJP playbook. Hate drives more people to the polling booth than warm, fuzzy feelings of pluralism. But, the question is, are the Congress leaders really conspiring with Pakistan to make sure the BJP lose?

Answer: It doesn’t matter.

No care for truth

In 1986, the philosopher Harry G. Frankfurt wrote an essay named “On Bullshit”, which was published as a book in 2005 and became a surprise bestseller. The book attempts to arrive at “a theoretical understanding of bullshit”. The key difference between a liar and a , ‘bullshitter’, Frankfurt tells us, is that the liar knows the truth and aims to deceive. The ‘bullshitter’, on the other hand, doesn’t care about the truth. He is “neither on the side of the true nor on the side of the false,” in Frankfurt’s words. “His eye is not on the facts at all, as the eyes of the honest man and of the liar are, except insofar as they may be pertinent to his interest in getting away with what he says.”

The ‘bullshitter’ is wise, for he has cottoned on to an important truth that has become more and more glaring in these modern times: that facts don’t matter. And to understand why, I ask you to go back with me in time to another seminal book, this one published in 1922.

The first chapter of “Public Opinion”, by the American journalist, Walter Lippmann, is titled “The World Outside and the Pictures in Our Heads”. In it, Lippmann makes the point that all of us have a version of the world inside our heads that resembles, but is not identical to, the world as it is. “The real environment,” he writes, “is altogether too big, too complex, and too fleeting for direct acquaintance.”

We construct a version of the world in our heads, and feed that version, for modifying it too much will require too much effort. If facts conflict with it, we ignore those facts, and accept only those that conform to our worldview. (Cognitive psychologists call this the “Confirmation Bias”.)

Lippmann sees this as a challenge for democracy, for how are we to elect our leaders if we cannot comprehend the impact they will have on the world?

Fragmented media

I would argue that this is a far greater problem today than it was in Lippmann’s time. Back then, and until a couple of decades ago, there was a broad consensus on the truth. There were gatekeepers to information and knowledge. Even accounting for biases, the mainstream media agreed on some basic facts. That has changed. The media is fragmented, there are no barriers to entry, and the mainstream media no longer has a monopoly of the dissemination of information. This is a good thing, with one worrying side effect: whatever beliefs or impulses we might have — the earth is flat, the Jews carried out 9/11, India is a Hindu nation — we can find plenty of “evidence” for it online, and connect with like-minded people. Finding others who share our beliefs makes us more strident, and soon we form multiple echo chambers that become more and more extreme. Polarisation increases. The space in the middle disappears. And the world inside our heads, shared by so many other, becomes impervious to facts.

This also means that impulses we would otherwise not express in polite society find validation, and a voice. Here’s another book you should read: in 1997, the sociologist, Timur Kuran, wrote “Private Truths, Public Lies” in which he coined the term “Preference Falsification”. There are many things we feel or believe but do not express because we fear social approbation. But as soon as we realise that others share our views, we are emboldened to express ourselves. This leads to a “Preference Cascade”: Kuran gives the example of the collapse of the Soviet Union, but an equally apt modern illustration is the rise of right-wing populists everywhere. I believe — and I apologise if this is too depressing to contemplate — that the majority of us are bigots, misogynists, racists, and tribal in our thinking. We have always been this way, but because liberal elites ran the media, and a liberal consensus seemed to prevail, we did not express these feelings. Social media showed us that we were not alone, and gave us the courage to express ourselves.

That’s where Donald Trump comes from. That’s where Mr. Modi comes from. Our masses vote for these fine gentlemen not in spite of their bigotry and misogyny, but because of it. Mr. Trump and Mr. Modi provide them a narrative that feeds the world inside their heads. Mexicans are rapists, foreigners are bad, Muslims are stealing our girls, gaumutra cures cancer — and so on. The truth is irrelevant. Facts. Don’t. Matter.

Think about the implication of this. This means that the men and women who wrote the Constitution were an out-of-touch elite, and the values they embedded in it were not shared by most of the nation. (As a libertarian, I think the Constitution was deeply flawed because it did not do enough to protect individual rights, but our society’s consensus would probably be that it did too much.) The “Idea of India” that these elites spoke of was never India’s Idea of India. These “liberal” values were imposed on an unwilling nation — and is such imposition, ironically, not deeply illiberal itself? This is what I call The Liberal Paradox.

All the ugliness in our politics today is the ugliness of the human condition. This is how we are. This is not a perversion of democracy but an expression of it. Those of us who are saddened by it — the liberal elites, libertarians like me — have to stop feeling entitled, and get down to work. The alt-right guru Andrew Breitbart once said something I never get tired of quoting: “Politics is downstream from Culture.” A political victory will now not come until there is a social revolution. Where will it begin?

Arun Shourie: Modi Government is Untruthful

Dead Cats - Fatal attraction of fake facts sours political debate

Tim Harford in The Financial Times


He did it again: Boris Johnson, UK foreign secretary, exhumed the old referendum-campaign lie that leaving the EU would free up £350m a week for the National Health Service. I think we can skip the well-worn details, because while the claim is misleading, its main purpose is not to mislead but to distract. The growing popularity of this tactic should alarm anyone who thinks that the truth still matters. 

You don’t need to take my word for it that distraction is the goal. A few years ago, a cynical commentator described the “dead cat” strategy, to be deployed when losing an argument at a dinner party: throw a dead cat on the table. The awkward argument will instantly cease, and everyone will start losing their minds about the cat. The cynic’s name was Boris Johnson. 

The tactic worked perfectly in the Brexit referendum campaign. Instead of a discussion of the merits and disadvantages of EU membership, we had a frenzied dead-cat debate over the true scale of EU membership fees. Without the steady repetition of a demonstrably false claim, the debate would have run out of oxygen and we might have enjoyed a discussion of the issues instead. 

My point is not to refight the referendum campaign. (Mr Johnson would like to, which itself is telling.) There’s more at stake here than Brexit: bold lies have become the dead cat of modern politics on both sides of the Atlantic. Too many politicians have discovered the attractions of the flamboyant falsehood — and why not? The most prominent of them sits in the White House. Dramatic lies do not always persuade, but they do tend to change the subject — and that is often enough. 

It is hard to overstate how corrosive this development is. Reasoned conversation becomes impossible; the debaters hardly have time to clear their throats before a fly-blown moggie hits the table with a rancid thud. 

Nor is it easy to neutralise a big, politicised lie. Trustworthy nerds can refute it, of course: the fact-checkers, the independent think-tanks, or statutory bodies such as the UK Statistics Authority. But a politician who is unafraid to lie is also unafraid to smear these organisations with claims of bias or corruption — and then one problem has become two. The Statistics Authority and other watchdogs need to guard jealously their reputation for truthfulness; the politicians they contradict often have no such reputation to worry about. 

Researchers have been studying the problem for years, after noting how easily charlatans could debase the discussion of smoking, vaccination and climate change. A good starting point is The Debunking Handbook by John Cook and Stephan Lewandowsky, which summarises a dispiriting set of discoveries. 

One problem that fact-checkers face is the “familiarity effect”: the endless arguments over the £350m-a-week lie (or Barack Obama’s birthplace, or the number of New Jersey residents who celebrated the destruction of the World Trade Center) is that the very process of rebutting the falsehood ensures that it is repeated over and over again. Even someone who accepts that the lie is a lie would find it much easier to remember than the truth. 

A second obstacle is the “backfire effect”. My son is due to get a flu vaccine this week, and some parents at his school are concerned that the flu vaccine may cause flu. It doesn’t. But in explaining that I risk triggering other concerns: who can trust Big Pharma these days? Shouldn’t kids be a bit older before being exposed to these strange chemicals? Some (not all) studies suggest that the process of refuting the narrow concern can actually harden the broader worldview behind it. 

Dan Kahan, professor of law and psychology at Yale, points out that issues such as vaccination or climate change — or for that matter, the independence of the UK Statistics Authority — do not become politicised by accident. They are dragged into the realm of polarised politics because it suits some political entrepreneur to do so. For a fleeting partisan advantage, Donald Trump has falsely claimed that vaccines cause autism. Children will die as a result. And once the intellectual environment has become polluted and polarised in this way, it’s extraordinarily difficult to draw the poison out again. 

This is a damaging game indeed. All of us tend to think tribally about politics: we absorb the opinions of those around us. But tribal thinking pushes us to be not only a Republican but also a Republican and a vaccine sceptic. One cannot be just for Brexit; one must be for Brexit and against the UK Statistics Authority. Of course it is possible to resist such all-encompassing polarisation, and many people do. But the pull of tribal thinking on all of us is strong. 

There are defences against the dead cat strategy. With skill, a fact-check may debunk a false claim without accidentally reinforcing it. But the strongest defence is an electorate that cares, that has more curiosity about the way the world really works than about cartoonish populists. If we let politicians drag facts into their swamp, we are letting them tug at democracy’s foundations.

Whiplash: the myth that funds a £20bn gravy train

Patrick Collinson in The Guardian


Ten years ago I was in a country lane in Leicestershire, indicating to turn right to go into a hotel for a family event. Seconds later my car was a write-off after a young driver careered round the bend, smashing into the rear of my VW Golf. Fortunately I stepped out uninjured. And from that moment I was pestered, again and again, to make a false whiplash claim.

One of the hotel’s guests was first in. “You’ve got to get down the doctors, tell them your neck is really hurting. You’ll easily get £3,000,” said one (I’m summarising here). But my neck, while a little stiff, wasn’t in pain. Others told me I was mad not to apply. But a decade later there is no evidence the crash caused anything other than a mild sprain that lasted a couple of days. And certainly not deserving of the £3,000-£6,000 that is routinely paid out to “victims” of even the mildest of rear-end shunts.

Now one brave consultant neurosurgeon, who has carried out thousands of operations involving neck and back issues, has declared that whiplash is a myth, nothing more than a multibillion-pound gravy train for lawyers, doctors and the victims suffering from “mainly non-existent injuries”.

In a remarkable piece for the Irish Times, Dr Charles Marks, a lecturer at University College Cork, says the medical profession is as guilty as the lawyers. “For 20 years I wrote medical reports which were economical with the truth … the truth being, there was very little wrong with the vast majority of compensation claimants that I saw. I was moving with the herd.” In Ireland, where payouts have reached levels that even the most avaricious ambulance-chasing lawyer here can only dream of, a doctor can earn as much as £3,000 a week in fees after spending 20 minutes with someone involved in a minor car crash, then writing a largely templated report. “It’s a fee of around €350 and you can easily do 10 a week,” Marks says.

Yet whiplash is “almost impossible to prove”, says Dr Marks, with patients self-diagnosing pain that can never be detected using sophisticated imaging techniques such as MRI and bone scans. “All whiplash is minor. Moderate or permanent whiplash is simply non-existent.”

He cites one study of 40 “demolition derby” drivers in the US who had an average of 1,500 collisions each over a couple of years. Compare that to a mild shunt in slow-moving traffic that, somehow, warrants payouts of thousands. Yet just two of the demolition derby drivers reported post-participation neck pain that lasted more than three months.

Dr Marks adds that in Greece and Lithuania, where there is no expectation of financial gain from whiplash, chronic neck pain following a car crash appears simply not to exist.

But one (British) consultant in Ireland is barely sufficient evidence. So I spoke to another whiplash expert, Dr Stuart Matthews, consultant surgeon in major orthopaedic trauma at the Leeds Teaching Hospitals. He sounded even more dismissive than Dr Marks. “There is not a single test that shows abnormality directly attributable to this condition. Diagnoses are purely on the say-so of the person involved. Many orthopaedic surgeons do not believe it is a genuine condition.”

He says early research that provided medical endorsement for whiplash claims has subsequently been rejected. “It’s the emperor’s new clothes. People just go along with it, there is a bandwagon.”

Neck sprain is genuine, he says, but recovery is relatively quick with little evidence of significant physical injury.

Yet the victims of whiplash receive £2bn a year in payouts, a fair chunk of which goes to personal injury lawyers. That’s £20bn over the past decade, paid for out of galloping increases in car insurance premiums. The forthcoming election means that reforms to whiplash payouts, promised in the prison and courts bill, have been shelved.

A new government, of whatever complexion, should reinstate the reforms – and order a major medical review to determine if we have all been conned for years.

Despite his lies, Donald Trump is a potent truth-teller

James S Gordon in The Guardian


Donald Trump evokes a wily and resilient mythic figure: the joker, the trickster, the fool, the one the Lakota people call the Heyoka, the contrary. Had his opponents – like Hillary Clinton – understood this quality in him, the electoral outcome might have been different. The sooner the rest of us understand this side of him, the better.

In the European tradition, the fool holds up the mirror to the monarch and to all of us, mocking our faults and pretensions. He (the fool is almost always a man) is not constrained by deference or allegiance to truth. The Heyoka, one of the purest forms of fool, pretends to shiver when everyone else is sweating and takes off his clothes in winter.


The fool is a potent truth-teller and commands attention. Shakespeare knew this. Lear’s Fool, a gentle version of the species, skewered the arrogance and pride that were his master’s downfall, even as he comforted him. The “scabrous” Thersites in Troilus and Cressida speaks with relentless, scene-stealing venom. He paints Achilles, the Greeks’ greatest hero, as a petulant adolescent; King Agamemnon is a blowhard, Helen of Troy a hooker.

The fool is always addressing us, his audience, as well as his high-ranking targets. He performs a vital social function, forcing us to examine our own preconceptions, especially our inflated ideas about our own virtue. Trump was telling all of us – women and minorities, progressives, pillars of the establishment, as well as his supporters – that we were just like him.

The appropriate, time-honored response to the fool’s sallies is to take instruction from them. Only after we’ve acknowledged and accepted our own shortcomings, do we have the integrity that allows us to keep him in his place. Perhaps if Secretary Clinton had been a more skillful, poised and humble warrior, she could have done this.

Fools serve the collective order by challenging those whose ignorance and blindness threaten it. They are meant to be instruments of awareness, not rulers. Impossible to imagine Lear’s Fool succeeding him or Thersites commanding the Greek army. Trump will not address his own limitations, cannot tolerate criticism, and takes himself dangerously seriously. This makes him is a seriously flawed fool. He believes his own hyperbole and threatens democratic order.

In the weeks since his election, Trump has continued to act the fool. Now, however, the underdog’s challenges have become a bully’s beatdowns. His attack on the steelworkers’ union leader, Chuck Jones, exactly the kind of man whom he claimed to champion, was a vicious and painful lie. Unfunny, purely ugly. His more recent rants, including boasts about the crowds at his inaugural and the millions of imaginary illegal Clinton voters, illuminate his own troubled insecurity: the all-powerful winner acting the petulant, powerless loser.

Many of President Trump’s cabinet choices are like the punchlines of jokes, but punchlines with potentially devastating real-world consequences: an Education Secretary who disparages public education and badly botched her own effort at creating an alternative; men charged with responding to climate change who deny its existence and a National Security Advisor who purveys paranoid fantasies.

There are glimmers of hope that the jester might mature to majesty. General Mattis, the Defense Secretary, inspired a Trumpian epiphany that waterboarding might be counterproductive. Conversations with Al Gore or, more likely, ones with his daughter, Ivanka, could persuade him to open his eyes to the reality of climate change.

Or perhaps President Trump will implode, brought down by the damage done by perverse cabinet choices, or words and actions so intemperate and ill-advised that Congress and the courts call him to a terminal account. His challenged immigration order could be a harbinger.

Meanwhile, what are the rest of us to do? The fact that this question is even being asked is healthy, a residual benefit of his fool’s vocation. Trump’s grand and vulgar self-absorption is inviting all of us to examine our own selfishness. His ignorance calls us to attend to our own blind spots. The fears that he stokes and the isolation he promotes goad us to be braver, more generous. 

Already, people all over the United States – Republicans I know as well as Democrats – are beginning to link inner awareness to small and great political action.

The day after Trump’s inauguration, hundreds of thousands of women of all ages, ethnicities, and political affiliations affirmed their rights, celebrated their community and slyly poked at the joker: “if I incorporated my uterus,” read one demonstrator’s sign, “would you stop trying to regulate it.”

The joker who is now our president has served an important function, waking us up to what we’ve not yet admitted in ourselves or accomplished in our country. He is, without realizing it, challenging us to grow in self-awareness, to act in ways that respect and fulfill what is best in ourselves and our democracy.

It’s time for us citizens, who’ve watched the performance, to take the stage.

How statistics lost their power

William Davies in The Guardian


In theory, statistics should help settle arguments. They ought to provide stable reference points that everyone – no matter what their politics – can agree on. Yet in recent years, divergent levels of trust in statistics has become one of the key schisms that have opened up in western liberal democracies. Shortly before the November presidential election, a study in the US discovered that 68% of Trump supporters distrusted the economic data published by the federal government. In the UK, a research project by Cambridge University and YouGov looking at conspiracy theories discovered that 55% of the population believes that the government “is hiding the truth about the number of immigrants living here”.

Rather than diffusing controversy and polarisation, it seems as if statistics are actually stoking them. Antipathy to statistics has become one of the hallmarks of the populist right, with statisticians and economists chief among the various “experts” that were ostensibly rejected by voters in 2016. Not only are statistics viewed by many as untrustworthy, there appears to be something almost insulting or arrogant about them. Reducing social and economic issues to numerical aggregates and averages seems to violate some people’s sense of political decency.

Nowhere is this more vividly manifest than with immigration. The thinktank British Future has studied how best to win arguments in favour of immigration and multiculturalism. One of its main findings is that people often respond warmly to qualitative evidence, such as the stories of individual migrants and photographs of diverse communities. But statistics – especially regarding alleged benefits of migration to Britain’s economy – elicit quite the opposite reaction. People assume that the numbers are manipulated and dislike the elitism of resorting to quantitative evidence. Presented with official estimates of how many immigrants are in the country illegally, a common response is to scoff. Far from increasing support for immigration, British Future found, pointing to its positive effect on GDP can actually make people more hostile to it. GDP itself has come to seem like a Trojan horse for an elitist liberal agenda. Sensing this, politicians have now largely abandoned discussing immigration in economic terms.

All of this presents a serious challenge for liberal democracy. Put bluntly, the British government – its officials, experts, advisers and many of its politicians – does believe that immigration is on balance good for the economy. The British government did believe that Brexit was the wrong choice. The problem is that the government is now engaged in self-censorship, for fear of provoking people further.

This is an unwelcome dilemma. Either the state continues to make claims that it believes to be valid and is accused by sceptics of propaganda, or else, politicians and officials are confined to saying what feels plausible and intuitively true, but may ultimately be inaccurate. Either way, politics becomes mired in accusations of lies and cover-ups.

The declining authority of statistics – and the experts who analyse them – is at the heart of the crisis that has become known as “post-truth” politics. And in this uncertain new world, attitudes towards quantitative expertise have become increasingly divided. From one perspective, grounding politics in statistics is elitist, undemocratic and oblivious to people’s emotional investments in their community and nation. It is just one more way that privileged people in London, Washington DC or Brussels seek to impose their worldview on everybody else. From the opposite perspective, statistics are quite the opposite of elitist. They enable journalists, citizens and politicians to discuss society as a whole, not on the basis of anecdote, sentiment or prejudice, but in ways that can be validated. The alternative to quantitative expertise is less likely to be democracy than an unleashing of tabloid editors and demagogues to provide their own “truth” of what is going on across society.

Is there a way out of this polarisation? Must we simply choose between a politics of facts and one of emotions, or is there another way of looking at this situation? One way is to view statistics through the lens of their history. We need to try and see them for what they are: neither unquestionable truths nor elite conspiracies, but rather as tools designed to simplify the job of government, for better or worse. Viewed historically, we can see what a crucial role statistics have played in our understanding of nation states and their progress. This raises the alarming question of how – if at all – we will continue to have common ideas of society and collective progress, should statistics fall by the wayside.

In the second half of the 17th century, in the aftermath of prolonged and bloody conflicts, European rulers adopted an entirely new perspective on the task of government, focused upon demographic trends – an approach made possible by the birth of modern statistics. Since ancient times, censuses had been used to track population size, but these were costly and laborious to carry out and focused on citizens who were considered politically important (property-owning men), rather than society as a whole. Statistics offered something quite different, transforming the nature of politics in the process.

Statistics were designed to give an understanding of a population in its entirety,rather than simply to pinpoint strategically valuable sources of power and wealth. In the early days, this didn’t always involve producing numbers. In Germany, for example (from where we get the term Statistik) the challenge was to map disparate customs, institutions and laws across an empire of hundreds of micro-states. What characterised this knowledge as statistical was its holistic nature: it aimed to produce a picture of the nation as a whole. Statistics would do for populations what cartography did for territory.

Equally significant was the inspiration of the natural sciences. Thanks to standardised measures and mathematical techniques, statistical knowledge could be presented as objective, in much the same way as astronomy. Pioneering English demographers such as William Petty and John Graunt adapted mathematical techniques to estimate population changes, for which they were hired by Oliver Cromwell and Charles II.

The emergence in the late 17th century of government advisers claiming scientific authority, rather than political or military acumen, represents the origins of the “expert” culture now so reviled by populists. These path-breaking individuals were neither pure scholars nor government officials, but hovered somewhere between the two. They were enthusiastic amateurs who offered a new way of thinking about populations that privileged aggregates and objective facts. Thanks to their mathematical prowess, they believed they could calculate what would otherwise require a vast census to discover.

There was initially only one client for this type of expertise, and the clue is in the word “statistics”. Only centralised nation states had the capacity to collect data across large populations in a standardised fashion and only states had any need for such data in the first place. Over the second half of the 18th century, European states began to collect more statistics of the sort that would appear familiar to us today. Casting an eye over national populations, states became focused upon a range of quantities: births, deaths, baptisms, marriages, harvests, imports, exports, price fluctuations. Things that would previously have been registered locally and variously at parish level became aggregated at a national level.

New techniques were developed to represent these indicators, which exploited both the vertical and horizontal dimensions of the page, laying out data in matrices and tables, just as merchants had done with the development of standardised book-keeping techniques in the late 15th century. Organising numbers into rows and columns offered a powerful new way of displaying the attributes of a given society. Large, complex issues could now be surveyed simply by scanning the data laid out geometrically across a single page.

These innovations carried extraordinary potential for governments. By simplifying diverse populations down to specific indicators, and displaying them in suitable tables, governments could circumvent the need to acquire broader detailed local and historical insight. Of course, viewed from a different perspective, this blindness to local cultural variability is precisely what makes statistics vulgar and potentially offensive. Regardless of whether a given nation had any common cultural identity, statisticians would assume some standard uniformity or, some might argue, impose that uniformity upon it.

Not every aspect of a given population can be captured by statistics. There is always an implicit choice in what is included and what is excluded, and this choice can become a political issue in its own right. The fact that GDP only captures the value of paid work, thereby excluding the work traditionally done by women in the domestic sphere, has made it a target of feminist critique since the 1960s. In France, it has been illegal to collect census data on ethnicity since 1978, on the basis that such data could be used for racist political purposes. (This has the side-effect of making systemic racism in the labour market much harder to quantify.)

Despite these criticisms, the aspiration to depict a society in its entirety, and to do so in an objective fashion, has meant that various progressive ideals have been attached to statistics. The image of statistics as a dispassionate science of society is only one part of the story. The other part is about how powerful political ideals became invested in these techniques: ideals of “evidence-based policy”, rationality, progress and nationhood grounded in facts, rather than in romanticised stories.

Since the high-point of the Enlightenment in the late 18th century, liberals and republicans have invested great hope that national measurement frameworks could produce a more rational politics, organised around demonstrable improvements in social and economic life. The great theorist of nationalism, Benedict Anderson, famously described nations as “imagined communities”, but statistics offer the promise of anchoring this imagination in something tangible. Equally, they promise to reveal what historical path the nation is on: what kind of progress is occurring? How rapidly? For Enlightenment liberals, who saw nations as moving in a single historical direction, this question was crucial.

The potential of statistics to reveal the state of the nation was seized in post-revolutionary France. The Jacobin state set about imposing a whole new framework of national measurement and national data collection. The world’s first official bureau of statistics was opened in Paris in 1800. Uniformity of data collection, overseen by a centralised cadre of highly educated experts, was an integral part of the ideal of a centrally governed republic, which sought to establish a unified, egalitarian society.

From the Enlightenment onwards, statistics played an increasingly important role in the public sphere, informing debate in the media, providing social movements with evidence they could use. Over time, the production and analysis of such data became less dominated by the state. Academic social scientists began to analyse data for their own purposes, often entirely unconnected to government policy goals. By the late 19th century, reformers such as Charles Booth in London and WEB Du Bois in Philadelphia were conducting their own surveys to understand urban poverty.

 

Illustration by Guardian Design

To recognise how statistics have been entangled in notions of national progress, consider the case of GDP. GDP is an estimate of the sum total of a nation’s consumer spending, government spending, investments and trade balance (exports minus imports), which is represented in a single number. This is fiendishly difficult to get right, and efforts to calculate this figure began, like so many mathematical techniques, as a matter of marginal, somewhat nerdish interest during the 1930s. It was only elevated to a matter of national political urgency by the second world war, when governments needed to know whether the national population was producing enough to keep up the war effort. In the decades that followed, this single indicator, though never without its critics, took on a hallowed political status, as the ultimate barometer of a government’s competence. Whether GDP is rising or falling is now virtually a proxy for whether society is moving forwards or backwards.

Or take the example of opinion polling, an early instance of statistical innovation occurring in the private sector. During the 1920s, statisticians developed methods for identifying a representative sample of survey respondents, so as to glean the attitudes of the public as a whole. This breakthrough, which was first seized upon by market researchers, soon led to the birth of the opinion polling. This new industry immediately became the object of public and political fascination, as the media reported on what this new science told us about what “women” or “Americans” or “manual labourers” thought about the world.

Nowadays, the flaws of polling are endlessly picked apart. But this is partly due to the tremendous hopes that have been invested in polling since its origins. It is only to the extent that we believe in mass democracy that we are so fascinated or concerned by what the public thinks. But for the most part it is thanks to statistics, and not to democratic institutions as such, that we can know what the public thinks about specific issues. We underestimate how much of our sense of “the public interest” is rooted in expert calculation, as opposed to democratic institutions.

As indicators of health, prosperity, equality, opinion and quality of life have come to tell us who we are collectively and whether things are getting better or worse, politicians have leaned heavily on statistics to buttress their authority. Often, they lean too heavily, stretching evidence too far, interpreting data too loosely, to serve their cause. But that is an inevitable hazard of the prevalence of numbers in public life, and need not necessarily trigger the type of wholehearted rejections of expertise that we have witnessed recently.

In many ways, the contemporary populist attack on “experts” is born out of the same resentment as the attack on elected representatives. In talking of society as a whole, in seeking to govern the economy as a whole, both politicians and technocrats are believed to have “lost touch” with how it feels to be a single citizen in particular. Both statisticians and politicians have fallen into the trap of “seeing like a state”, to use a phrase from the anarchist political thinker James C Scott. Speaking scientifically about the nation – for instance in terms of macroeconomics – is an insult to those who would prefer to rely on memory and narrative for their sense of nationhood, and are sick of being told that their “imagined community” does not exist.

On the other hand, statistics (together with elected representatives) performed an adequate job of supporting a credible public discourse for decades if not centuries. What changed?

The crisis of statistics is not quite as sudden as it might seem. For roughly 450 years, the great achievement of statisticians has been to reduce the complexity and fluidity of national populations into manageable, comprehensible facts and figures. Yet in recent decades, the world has changed dramatically, thanks to the cultural politics that emerged in the 1960s and the reshaping of the global economy that began soon after. It is not clear that the statisticians have always kept pace with these changes. Traditional forms of statistical classification and definition are coming under strain from more fluid identities, attitudes and economic pathways. Efforts to represent demographic, social and economic changes in terms of simple, well-recognised indicators are losing legitimacy.

Consider the changing political and economic geography of nation states over the past 40 years. The statistics that dominate political debate are largely national in character: poverty levels, unemployment, GDP, net migration. But the geography of capitalism has been pulling in somewhat different directions. Plainly globalisation has not rendered geography irrelevant. In many cases it has made the location of economic activity far more important, exacerbating the inequality between successful locations (such as London or San Francisco) and less successful locations (such as north-east England or the US rust belt). The key geographic units involved are no longer nation states. Rather, it is cities, regions or individual urban neighbourhoods that are rising and falling.

The Enlightenment ideal of the nation as a single community, bound together by a common measurement framework, is harder and harder to sustain. If you live in one of the towns in the Welsh valleys that was once dependent on steel manufacturing or mining for jobs, politicians talking of how “the economy” is “doing well” are likely to breed additional resentment. From that standpoint, the term “GDP” fails to capture anything meaningful or credible.

When macroeconomics is used to make a political argument, this implies that the losses in one part of the country are offset by gains somewhere else. Headline-grabbing national indicators, such as GDP and inflation, conceal all sorts of localised gains and losses that are less commonly discussed by national politicians. Immigration may be good for the economy overall, but this does not mean that there are no local costs at all. So when politicians use national indicators to make their case, they implicitly assume some spirit of patriotic mutual sacrifice on the part of voters: you might be the loser on this occasion, but next time you might be the beneficiary. But what if the tables are never turned? What if the same city or region wins over and over again, while others always lose? On what principle of give and take is that justified?

In Europe, the currency union has exacerbated this problem. The indicators that matter to the European Central Bank (ECB), for example, are those representing half a billion people. The ECB is concerned with the inflation or unemployment rate across the eurozone as if it were a single homogeneous territory, at the same time as the economic fate of European citizens is splintering in different directions, depending on which region, city or neighbourhood they happen to live in. Official knowledge becomes ever more abstracted from lived experience, until that knowledge simply ceases to be relevant or credible.

The privileging of the nation as the natural scale of analysis is one of the inbuilt biases of statistics that years of economic change has eaten away at. Another inbuilt bias that is coming under increasing strain is classification. Part of the job of statisticians is to classify people by putting them into a range of boxes that the statistician has created: employed or unemployed, married or unmarried, pro-Europe or anti-Europe. So long as people can be placed into categories in this way, it becomes possible to discern how far a given classification extends across the population.

This can involve somewhat reductive choices. To count as unemployed, for example, a person has to report to a survey that they are involuntarily out of work, even if it may be more complicated than that in reality. Many people move in and out of work all the time, for reasons that might have as much to do with health and family needs as labour market conditions. But thanks to this simplification, it becomes possible to identify the rate of unemployment across the population as a whole.

Here’s a problem, though. What if many of the defining questions of our age are not answerable in terms of the extent of people encompassed, but the intensity with which people are affected? Unemployment is one example. The fact that Britain got through the Great Recession of 2008-13 without unemployment rising substantially is generally viewed as a positive achievement. But the focus on “unemployment” masked the rise of underemployment, that is, people not getting a sufficient amount of work or being employed at a level below that which they are qualified for. This currently accounts for around 6% of the “employed” labour force. Then there is the rise of the self-employed workforce, where the divide between “employed” and “involuntarily unemployed” makes little sense.

This is not a criticism of bodies such as the Office for National Statistics (ONS), which does now produce data on underemployment. But so long as politicians continue to deflect criticism by pointing to the unemployment rate, the experiences of those struggling to get enough work or to live on their wages go unrepresented in public debate. It wouldn’t be all that surprising if these same people became suspicious of policy experts and the use of statistics in political debate, given the mismatch between what politicians say about the labour market and the lived reality.

The rise of identity politics since the 1960s has put additional strain on such systems of classification. Statistical data is only credible if people will accept the limited range of demographic categories that are on offer, which are selected by the expert not the respondent. But where identity becomes a political issue, people demand to define themselves on their own terms, where gender, sexuality, race or class is concerned.

Opinion polling may be suffering for similar reasons. Polls have traditionally captured people’s attitudes and preferences, on the reasonable assumption that people will behave accordingly. But in an age of declining political participation, it is not enough simply to know which box someone would prefer to put an “X” in. One also needs to know whether they feel strongly enough about doing so to bother. And when it comes to capturing such fluctuations in emotional intensity, polling is a clumsy tool.

Statistics have faced criticism regularly over their long history. The challenges that identity politics and globalisation present to them are not new either. Why then do the events of the past year feel quite so damaging to the ideal of quantitative expertise and its role in political debate?

In recent years, a new way of quantifying and visualising populations has emerged that potentially pushes statistics to the margins, ushering in a different era altogether. Statistics, collected and compiled by technical experts, are giving way to data that accumulates by default, as a consequence of sweeping digitisation. Traditionally, statisticians have known which questions they wanted to ask regarding which population, then set out to answer them. By contrast, data is automatically produced whenever we swipe a loyalty card, comment on Facebook or search for something on Google. As our cities, cars, homes and household objects become digitally connected, the amount of data we leave in our trail will grow even greater. In this new world, data is captured first and research questions come later.

In the long term, the implications of this will probably be as profound as the invention of statistics was in the late 17th century. The rise of “big data” provides far greater opportunities for quantitative analysis than any amount of polling or statistical modelling. But it is not just the quantity of data that is different. It represents an entirely different type of knowledge, accompanied by a new mode of expertise.

First, there is no fixed scale of analysis (such as the nation) nor any settled categories (such as “unemployed”). These vast new data sets can be mined in search of patterns, trends, correlations and emergent moods. It becomes a way of tracking the identities that people bestow upon themselves (such as “#ImwithCorbyn” or “entrepreneur”) rather than imposing classifications upon them. This is a form of aggregation suitable to a more fluid political age, in which not everything can be reliably referred back to some Enlightenment ideal of the nation state as guardian of the public interest.

Second, the majority of us are entirely oblivious to what all this data says about us, either individually or collectively. There is no equivalent of an Office for National Statistics for commercially collected big data. We live in an age in which our feelings, identities and affiliations can be tracked and analysed with unprecedented speed and sensitivity – but there is nothing that anchors this new capacity in the public interest or public debate. There are data analysts who work for Google and Facebook, but they are not “experts” of the sort who generate statistics and who are now so widely condemned. The anonymity and secrecy of the new analysts potentially makes them far more politically powerful than any social scientist.

A company such as Facebook has the capacity to carry quantitative social science on hundreds of billions of people, at very low cost. But it has very little incentive to reveal the results. In 2014, when Facebook researchers published results of a study of “emotional contagion” that they had carried out on their users – in which they altered news feeds to see how it affected the content that users then shared in response – there was an outcry that people were being unwittingly experimented on. So, from Facebook’s point of view, why go to all the hassle of publishing? Why not just do the study and keep quiet?

What is most politically significant about this shift from a logic of statistics to one of data is how comfortably it sits with the rise of populism. Populist leaders can heap scorn upon traditional experts, such as economists and pollsters, while trusting in a different form of numerical analysis altogether. Such politicians rely on a new, less visible elite, who seek out patterns from vast data banks, but rarely make any public pronouncements, let alone publish any evidence. These data analysts are often physicists or mathematicians, whose skills are not developed for the study of society at all. This, for example, is the worldview propagated by Dominic Cummings, former adviser to Michael Gove and campaign director of Vote Leave. “Physics, mathematics and computer science are domains in which there are real experts, unlike macro-economic forecasting,” Cummings has argued.

Figures close to Donald Trump, such as his chief strategist Steve Bannon and the Silicon Valley billionaire Peter Thiel, are closely acquainted with cutting-edge data analytics techniques, via companies such as Cambridge Analytica, on whose board Bannon sits. During the presidential election campaign, Cambridge Analytica drew on various data sources to develop psychological profiles of millions of Americans, which it then used to help Trump target voters with tailored messaging.

This ability to develop and refine psychological insights across large populations is one of the most innovative and controversial features of the new data analysis. As techniques of “sentiment analysis”, which detect the mood of large numbers of people by tracking indicators such as word usage on social media, become incorporated into political campaigns, the emotional allure of figures such as Trump will become amenable to scientific scrutiny. In a world where the political feelings of the general public are becoming this traceable, who needs pollsters?

Few social findings arising from this kind of data analytics ever end up in the public domain. This means that it does very little to help anchor political narrative in any shared reality. With the authority of statistics waning, and nothing stepping into the public sphere to replace it, people can live in whatever imagined community they feel most aligned to and willing to believe in. Where statistics can be used to correct faulty claims about the economy or society or population, in an age of data analytics there are few mechanisms to prevent people from giving way to their instinctive reactions or emotional prejudices. On the contrary, companies such as Cambridge Analytica treat those feelings as things to be tracked.

But even if there were an Office for Data Analytics, acting on behalf of the public and government as the ONS does, it is not clear that it would offer the kind of neutral perspective that liberals today are struggling to defend. The new apparatus of number-crunching is well suited to detecting trends, sensing the mood and spotting things as they bubble up. It serves campaign managers and marketers very well. It is less well suited to making the kinds of unambiguous, objective, potentially consensus-forming claims about society that statisticians and economists are paid for.

In this new technical and political climate, it will fall to the new digital elite to identify the facts, projections and truth amid the rushing stream of data that results. Whether indicators such as GDP and unemployment continue to carry political clout remains to be seen, but if they don’t, it won’t necessarily herald the end of experts, less still the end of truth. The question to be taken more seriously, now that numbers are being constantly generated behind our backs and beyond our knowledge, is where the crisis of statistics leaves representative democracy.

On the one hand, it is worth recognising the capacity of long-standing political institutions to fight back. Just as “sharing economy” platforms such as Uber and Airbnb have recently been thwarted by legal rulings (Uber being compelled to recognise drivers as employees, Airbnb being banned altogether by some municipal authorities), privacy and human rights law represents a potential obstacle to the extension of data analytics. What is less clear is how the benefits of digital analytics might ever be offered to the public, in the way that many statistical data sets are. Bodies such as the Open Data Institute, co-founded by Tim Berners-Lee, campaign to make data publicly available, but have little leverage over the corporations where so much of our data now accumulates. Statistics began life as a tool through which the state could view society, but gradually developed into something that academics, civic reformers and businesses had a stake in. But for many data analytics firms, secrecy surrounding methods and sources of data is a competitive advantage that they will not give up voluntarily.

A post-statistical society is a potentially frightening proposition, not because it would lack any forms of truth or expertise altogether, but because it would drastically privatise them. Statistics are one of many pillars of liberalism, indeed of Enlightenment. The experts who produce and use them have become painted as arrogant and oblivious to the emotional and local dimensions of politics. No doubt there are ways in which data collection could be adapted to reflect lived experiences better. But the battle that will need to be waged in the long term is not between an elite-led politics of facts versus a populist politics of feeling. It is between those still committed to public knowledge and public argument and those who profit from the ongoing disintegration of those things.