Lies, Damn Lies, and the Numbers They Choose
How statistics became the new battlefield of propaganda
There was a time when numbers were considered sacred — cold, clean, clinical — the benchmark of objectivity. They seemed to offer clarity, certainty, and a kind of universal language. However, in truth, numbers have always offered the illusion of truth, rather than its guarantee. In academic circles, data are meaningless until they are peer-reviewed, the methodology is explained, and the assumptions are thoroughly interrogated. Yet for the masses, numbers come pre-packaged, decontextualised, and weapon-ready. They don't ask where the data came from — they absorb what it appears to say. Those with some sense of ability to comprehend then go on to use the numbers to arm the methodology from the lens of their propagators for their gains, ideological views, or even biases.
In the age of data demagoguery, the battlefield isn’t fought with bullets; it’s fought with bar charts, hashtags, and half-truths.
Governments do it. Media corporations do it. Anonymous X-posters (formerly Twitter), eggs, and suit-clad MPs alike do it. They take the raw material of reality — data — and twist it until it screams something else entirely. The most effective weapon in modern discourse isn’t a lie. It’s a ‘statistical truth ripped from context and dropped like a grenade.’
Take "per capita," a phrase that now floats through political debate like an incantation. On the surface, it sounds fair, balanced, even scientific. In statistical terms, it simply means dividing a total figure by the number of people in a given population to allow for comparisons across groups or nations of different sizes. When used responsibly, it can be helpful, for instance, to compare healthcare spending across countries or identify resource needs by population density.
But that’s the theory. In practice, “per capita” becomes dangerous when used without context, curiosity, or conscience. It assumes that people are evenly distributed, equally responsible, and that the data collection is impartial. Yet in real-world applications, especially in crime or immigration debates, per capita often becomes a scalpel used not to dissect policy but to demonise entire communities. A single incident involving a minority group becomes a statistical 'spike', exaggerated by the small size of the denominator, while thousands of incidents involving the majority are flattened into statistical background noise.
Per capita should reveal disparities. Instead, it often conceals reality and fuels outrage, not because the math is wrong, but because the framing is weaponised.
If a single person in a small minority commits a crime, the per capita rate explodes. One offence becomes a "crisis" — a fabricated trend. Meanwhile, a hundred similar offences committed by the majority are swallowed into statistical silence.
Take this example: in a town of 1,000 people, where 10 are from a minority group, one offence committed by a minority individual results in a per capita rate of 10%. In contrast, if 50 offences are committed by the remaining 990, the per capita rate is just over 5%. The headlines? "Minority group twice as likely to offend." The truth? Fifty crimes were committed by the majority; one by the minority. But the framing changes everything.
Or take crime statistics from the Met Police in London, where over 12 months, 65% of knife crime suspects were white, yet public perception overwhelmingly associated knife crime with Black youth, in part due to repeated misuse of per capita stats that fail to account for stop-and-search bias, demographic density, and area-based surveillance.
The illusion? That the danger wears a certain skin, speaks a certain language, or prays a certain way. This is not mathematics. This is manipulation.
Look at how "grooming gangs" became synonymous with British Pakistani men, even though official police data shows the majority of suspects are white. According to the 2024 report from the National Police Chiefs' Council, 55% of grooming gang suspects were white British, while 12.9% were Pakistani. But only 31% of suspects had their ethnicity recorded. Despite this, even disruptive and political figures have used the smaller number to stoke fear, ignoring the larger pool of offenders.
It must be stated unequivocally: a crime is a crime, regardless of who commits it. Whether the perpetrator is white or brown, male or female, rich or poor, local or foreign, all must be held to the same legal standard. Justice, by definition, demands impartiality. We have laws not to excuse or shield anyone, but to ensure accountability. When institutions or individuals choose to conceal, dismiss, or selectively emphasise crime based on identity, they don’t protect society — they corrode it. They undermine victims and destroy the public’s trust in law and order.
This is why the principle must be absolute: all crimes are abhorrent. Prosecution should follow evidence, not ethnicity. From grooming gangs to COVID fraud, from malfeasance in public office to international war crimes — there can be no selective justice. There is no two ways about it.
During the pandemic, for instance, the media hammered home benefit fraud numbers. The DWP reported approximately £6.4 billion in fraud for the 2020–21 period. But what was quietly buried? The estimated £37 billion lost to corporate COVID loan fraud. Government communications, health authority briefings, and even international bodies leaned heavily on per capita framing to condition public compliance. Deaths per 100,000 were broadcast daily without adequate context on age, comorbidities, or testing inconsistencies. Countries were compared as though their health systems, populations, and reporting practices were identical. This wasn't just poor communication; it was a deliberate strategy.
The goal? To magnify fear, simplify outrage, and stifle dissent. A society panicked by numbers becomes more compliant. A population that fears being labelled irresponsible or 'anti-science' stops asking difficult questions. And amid that chaos, vast sums disappeared upwards — while rage was directed downwards. Media outrage was racialised, classed, and deflected, never focused on where the power lay.
When it comes to malfeasance in public office, the Post Office Horizon scandal is a devastating case in point. For over a decade, sub-postmasters were falsely prosecuted based on flawed IT evidence. Hundreds of lives were upended, reputations ruined, and justice denied. Despite irrefutable evidence of institutional failure, accountability was minimal and delayed. And yet, the statistical presentation of the scandal, which falsely balanced reports, obfuscated conviction figures, and delayed admissions, allowed the government and the Post Office to evade scrutiny for years.
This same tactic extends across Treasury reporting. Unemployment statistics are routinely sanitised by excluding those not actively seeking work, those on zero-hour contracts, or underemployed workers. Growth projections are framed as signs of economic recovery without accounting for inflation, wage stagnation, or regional disparities. Reports might boast a "0.3% quarterly GDP rise" while food bank use and homelessness hit record highs. It's not just spin. It's a statistical sleight of hand, used to divert attention away from the reality on the ground.
In all these cases, numbers are wielded not to inform but to redirect. Public anger is defused, misdirected, or outright suppressed through the use of selective datasets, cheerful graphs, and misleading comparisons. Whether it’s the Horizon scandal, welfare reform, or national growth, the larger picture remains the same: keep people focused on the numbers, but never on who gets to decide how they’re counted.
Internationally, the same selective lens applies. When Russia invaded Ukraine, Western leaders swiftly called for war crimes tribunals, citing per capita death tolls and population displacement to galvanise public support and justify sweeping foreign aid and military backing. But compare this to the treatment of Israel's ongoing actions in Gaza, where over 30,000 Palestinians, including thousands of children, have been killed according to UN estimates, and entire neighbourhoods flattened. Here, the numbers are buried under endless qualifications: 'Hamas targets', 'complex urban warfare', 'no verified totals.' Per capita? Rarely mentioned.
This isn't about denying tragedy in Ukraine. It's about how statistics are mobilised, or muted, to frame one conflict as a moral crusade and another as unfortunate 'complexity'. In Iraq, Afghanistan, and Yemen, NATO airstrikes and drone warfare left vast civilian casualties. But these were softened with bureaucratic language like 'collateral damage' or buried in aggregate figures that obscured scale.
In each case, the numbers are curated not to reveal horror, but to shape perception, amplifying some atrocities while erasing others. That is not data. That is doctrine with a spreadsheet.
This is the architecture of weaponised statistics:
Use selective sampling, like polling only certain areas that support your view.
Present percentages, not totals, saying "50% increase in crime" sounds alarming, even if it’s from 2 to 3 incidents.
Ignore denominator size when it helps you, one offence in a village of 100 is more alarming 'per capita' than 50 offences in a city of 10,000, but guess which gets the headline.
Pretend correlation is causation, like blaming video games for youth violence, because both happen at the same time.
And always, always say: “we’re just following the data”, even when the data is cherry-picked, outdated, or stripped of all context.
It’s the equivalent of showing a puddle and calling it a flood, or pointing to a cracked tile and declaring structural collapse. It’s like being told your income has increased by 5%, while the prices of bread, rent, and heating have risen by 25%. Numbers can be technically correct yet practically deceitful. It’s misdirection by decimal point, distortion by design, and manipulation through mathematical theatre.
Because data doesn’t speak for itself. People speak through data, and people lie.
This isn’t just a failure of statistical literacy. It’s data demagoguery, the manipulation of numbers to inflame passions, cement ideologies, and redirect scrutiny away from power. In the past, this practice was largely the domain of governments, intelligence agencies, and compliant media outlets. But today, as the authority of traditional informational gatekeepers erodes, a new chaos emerges. With open-access data and algorithmic amplification, anyone with a platform, including influencers, grifters, political operatives, and fringe groups, can manipulate a graph, misread a dataset, or reframe a statistic to serve their agenda.
Data demagoguery has gone viral. And instead of informing debate, it now fuels digital tribalism. A single misleading chart can circulate across millions of screens before any correction is issued — if it ever is. What used to be state-sponsored spin has become decentralised disinformation. The result? Not just confusion, but deliberate division. In this war of numbers, the truth is not only bent, it’s buried.
In an age where trust is collapsing and institutions are haemorrhaging legitimacy, weaponised statistics have become the sharpest tool left in the hands of those desperate to retain control. But this is only the beginning. As data becomes more open-source and software more accessible, the ability to distort reality is no longer confined to states or institutions; it belongs to anyone with a platform and an agenda.
This is the new frontier of data demagoguery: influencers manufacturing outrage with doctored charts, partisan accounts spinning datasets into cultural panic, and AI-powered misinformation bots crafting viral graphs faster than truth can catch up. From dodgy unemployment infographics on Instagram to wildly misleading vaccination statistics on Telegram, the battlefield isn’t policy, it’s perception, and the ammunition is always numbers.
The goal isn’t clarity, it’s chaos masked as truth, but in reality, it’s tribalism. As long as we’re busy accusing each other, we forget to ask the one question they fear most: Who built the system that fails us all, and why is it still standing?
The next time you hear "per capita," "trending upwards," "record highs," or "statistically significant," pause. Ask: Who’s speaking? What’s missing? What’s being hidden behind the mask of maths? Because every statistic carries an author, and every author a motive.
As George Orwell once warned, "Political language... is designed to make lies sound truthful and murder respectable." Today, the language isn’t just political. It’s statistical. It comes in charts, dashboards, and policy slides. What Orwell saw in words, we now face in numbers.
In this digital age, it is not just authoritarian regimes that weaponise data to rewrite truth. It is influencers with apps, bots with pie charts, and media figures presenting cherry-picked graphs with polished conviction. The next great propaganda isn’t a fiery speech. It’s a misleading infographic.
So listen closely, not just to what the numbers say, but to how they are arranged, who benefits from their telling, and who is erased in the process.
Because in this information war, the most dangerous falsehood isn’t a lie. It’s a truth bent just enough to turn neighbour against neighbour.