History Main / LiesDamnedLiesAndStatistics

See also NineOutOfTenDoctorsAgree, which is much a sub trope to this, and AbsoluteComparative, where the use of statistics is avoided entirely by comparing the product to nothing.

* A favorite tactic of the Creator/FoxNewsChannel is to refer to UsefulNotes/NewYorkCity, UsefulNotes/LosAngeles, and especially UsefulNotes/{{Chicago}} as {{Wretched Hive}}s due to their high number of murders. What the hosts conveniently neglect to mention is that crime statistics are measured per 100,000 residents, and presenting large raw numbers outside that context is misleading. In truth, America's three largest cities are [[https://en.wikipedia.org/wiki/List_of_United_States_cities_by_crime_rate not the most violent in the country by a longshot]].

* A BBC {{Edutainment}} series about mathematics in the real world once had a storyline in which one of the main characters got a job for an advertising company, asking the stores who had been advertising with them if they'd seen an increase in sales since they started. When she tries to explain to her boss that several of them had said they ''always'' got an increase in sales this time of year, he smugly informs her that this wasn't part of the question. When another character later tells her about a ''proper'' survey that actually tries to get the right answers, she's relieved to hear they're not ''all'' a con.

* A BBC {{Edutainment}} series about mathematics in the real world once had a storyline in which one of the main characters got a job for an advertising company, asking the stores who had been advertising with them if they'd seen an increase in sales since they started. When she tries to explain to her boss that several of them had said they ''always'' got an increase in sales this time of year, he smugly informed her that this wasn't part of the question. When another character later tells her about a ''proper'' survey that actually tries to get the right answers, she's relieved to hear they're not ''all'' a con.

** A somewhat more reasonable one is Godfrey's claim that since the League formed, "white-collar crime is up 3%!" While a more damning statistic, it's also the one kind of crime that the League ''doesn't'' get involved in much (not to mention it's a pretty small increase that could easily be unrelated).

* ''Series/FiringLine'': Buckley's 1981 interview with Dr. Thomas Sowell mainly focused on the misuse of statistics to allege institutional racism or sexism. As Sowell pointed out, the stats most often cited by those trying to push that particular political agenda are group-level comparisons of "wage gaps", but when one factors in variables such as age, experience, profession, and workload, those differences either diminish or disappear, or even show a disparity in the other direction.

* The ''WesternAnimation/JusticeLeague'' was asked: "Maybe you'd care to explain why on your watch, 50% of marriages now end in divorce and the other 50% end in death!" Aside from the fact that the same was true before the formation of the League, until the end of time, a significant portion of marriages will end in death, as people do have a tendency to die, married or not. That and one can get divorced multiple times - absent resurrection, one can only die once. A somewhat more reasonable one is Godfrey's claim that since the League formed, "white-collar crime is up 3%!" While a more damning statistic, it's also the one kind of crime that the League ''doesn't'' get involved in much (not to mention it's a pretty small increase that could easily be unrelated).

* Shizuo in ''{{LightNovel/Durarara}}'' maintains that the series's troll, Izaya, is behind "99% of all the weird crap" that goes on the setting. Sure, Shizuo can get irrational when mad, and will even use statistics and percents to maintain points. [[ProperlyParanoid ...Did we mention Izaya's a troll?]] We can only assume that he means ''every single weird thing''.

* A favorite tactic of the Creator/FoxNewsChannel is to refer to UsefulNotes/NewYorkCity, UsefulNotes/LosAngeles, and especially UsefulNotes/{{Chicago}} as {{Wretched Hive}}s due to their high number of crimes. What the hosts conveniently neglect to mention is that crime statistics are measured per 100,000 residents, and presenting large raw numbers outside that context is misleading. In truth, America's three largest cities are [[https://en.wikipedia.org/wiki/List_of_United_States_cities_by_crime_rate nowhere near the most violent in the country]].

* A favorite tactic of the Creator/FoxNewsChannel is to refer to UsefulNotes/NewYorkCity, UsefulNotes/LosAngeles, and especially UsefulNotes/{{Chicago}} as {{Wretched Hive}}s due to their high number of crimes. What the hosts conveniently neglect to mention is that crime statistics are measured per 100,000 residents, and large raw numbers mean relatively little in comparison to a large population. In truth, America's three largest cities are [[https://en.wikipedia.org/wiki/List_of_United_States_cities_by_crime_rate nowhere near the most violent in the country]].

* A favorite tactic of the Creator/FoxNewsChannel is to refer to UsefulNotes/NewYorkCity, UsefulNotes/LosAngeles, and especially UsefulNotes/{{Chicago}} as {{WretchedHive}}s due to their high number of crimes. What the hosts conveniently neglect to mention is that crime statistics are measured per 100,000 residents, and large raw numbers mean relatively little in comparison to a large population. In truth, America's three largest cities are [[https://en.wikipedia.org/wiki/List_of_United_States_cities_by_crime_rate nowhere near the most violent in the country]].

* Programs on Creator/AnimalPlanet are fond of citing how Americans spend more money annually on cat or dog food than on baby food. This is depicted as evidence that Americans pamper their pets like babies but overlooks several facts: that pets eat pet food for their entire lives, whereas babies only eat baby food for about a year and a half, and that many families have more than one pet at a time, but relatively few have more than one child of an age to eat baby food at the same time. Also, a baby often consumes breast milk from the mother which wouldn't show up in the statistics when calculating the cost of baby food. It also doesn't take into account the many people who make their own baby food, or feed their babies foods like applesauce or bananas that are ''already'' nice and soft; pet food is significantly harder to prepare.

[[folder:Real Life]]
* During UsefulNotes/WorldWarI, helmets were almost withdrawn from British soldiers. When Britain started issuing steel helmets to all soldiers on the western front in 1916, generals began to call for their removal as they increased incidences of headwounds twelvefold and doubled total casualties. The reason? If someone gets hit in the head by some woolly bear or flying frog (German H.E. or rifle grenade) shrapnel and ''lives'' it's a "head wound" and if they are unable to fight, the person is a "casualty"; if they ''die'' from a bullet in the brain, then they are a "fatality" and so don't appear on casualty statistics. Since helmets let more people survive, the number of head wounds soared. A politician used the numbers to support his position that "helmets are expensive and cause cowardice" (which, as it turns out is exactly the opposite of the kind of behavior helmets encourage, see below), and never explained what it really meant - doubly effective as most people don't know the difference between "casualty" and "fatality". The real justification behind the attempt to withdraw helmets narrowed down to, "all a dead soldier needs is a ''funeral''." A '''wounded''' soldier gets dragged out of combat by at least one of his buddies, and then provided weeks, months or even years of medical attention. From a [[AMillionIsAStatistic statistical standpoint]], adopting helmets drastically increased the effectiveness of enemy weapons - and a '''''lot''''' of UsefulNotes/WW1 generals genuinely believed in [[WeHaveReserves disposable]] [[ZergRush personnel]]. Luckily, more ethical parties changed the way they recorded casualties, or the helmets would likely have been recalled.
** Likewise, the number of cyclists being treated for head wounds have increased massively since wearing a helmet became more widespread. Of course this is because they previously wouldn't have survived the accident at all.
** Situations like the cyclists mentioned above also can also be attributed to the Peltzman Effect, aka Risk Compensation. This is the effect of a person being aware of greater safety ("I don't have to worry about hitting my head, I'm wearing a helmet!"), and taking greater risks due to the perceived increase in safety. So there actually ''could'' be an increase in injuries, due to more people taking greater risks.
** Sick as it was, UsefulNotes/WW1 quickly devolved into a stalemate, at which point it was clear that the loser would be whichever side ran out of men or money first. With sufficiently large draft pools, conserving resources was more important than conserving manpower.... until 1915, that is, when they began to realise just how ''shallow'' their manpower-reserves actually were relative to the demands of the war. By 1917 they were basically doing everything they could to conserve their manpower, to the point that they produced enough helmets to equip even the logistics personnel (i.e. people who ''might'' but probably won't be facing shrapnel, bullets, etc, etc).
* At some Reform Judaism synagogues, a popular "joke" to lead into the sermon is, "x% of deaths occur in a hospital, x% of deaths occur in a car, x% of deaths happen in the home...[continues on for a while] while there have been only ''three'' deaths in a synagogue, and no deaths ever reported while studying Torah! Clearly, the safest passion, therefore, is studying Torah."
* Pretty much the oldest trick in statistics is implying that because X is increasing/decreasing and Y is doing the same or the opposite, then X must be affecting Y[[note]]This is the logical fallacy of [[https://en.wikipedia.org/wiki/Correlation_does_not_imply_causation confusing correlation with causation]], unless of course this editor has committed the [[LogicBomb logical fallacy of confusing correlation (of topics) with causation (by fallacy)]][[/note]]. In reality, this isn't necessarily so unless you can manipulate one of the variables. Without other evidence Y could be causing X, or they could both be caused by Z. For example if someone presents a graph that shows street violence is increasing as video games are getting more violent,[[note]]Which isn't true but let's pretend[[/note]] they want you to think that violent games are causing street violence, but you can easily come to the conclusion that video games are becoming more violent as a reflection of a more violent society.
** For an example of X and Y being manipulated by Z, the number of pipes burst and the number of sweaters worn both go up as a result of freezing temperatures. Someone who hates sweaters could take this information and make it into a graph of pipes broken versus sweaters worn, and from that data alone it would appear wearing sweaters causes pipes to burst.
* A related strategy was used by US president UsefulNotes/RichardNixon to portray marijuana as a gateway drug. His anti-drug team estimated that 80% of marijuana users go on to use cocaine; actually, 80% of cocaine users had started with pot, but only about one in 2,400 marijuana users (just under 0.042%) go on to use cocaine. And related to that, most people willing to take a drug as dangerous as cocaine are willing to take a drug as relatively safe as marijuana. It's the same reason most marijuana smokers have drunk alcohol at some point, and why most alcohol drinkers have drunk something with caffeine in it. Water: the Gateway Liquid.
** One study cited by [[http://www.economist.com/blogs/dailychart/2010/11/drugs_cause_most_harm this article]] has experts ranking various drugs by their relative danger to individuals and those surrounding them. The original study appears to be fairly legitimate, yet the article commits a statistical sin of simply adding the value of 'harm to self' and 'harm to others' together to rank each drug. There is solid evidence that marijuana itself is very safe if vaped, as opposed to smoked. The same goes for nicotine, which was also found by the [[https://www.gov.uk/government/publications/e-cigarettes-an-evidence-update same study,]] commissioned by Public Health England, to be relatively harmless when the pure extract was vaporized. Alcohol is often perceived as less harmful than marijuana and cigarettes, despite the dangerous behaviours it can create.
* When UsefulNotes/RonaldReagan's Attorney General Edwin Meese wanted "proof" that pornography was evil, he created the Attorney General's Commission on Pornography. The commission members were a preselected cohort of anti-pornography campaigners. Not surprisingly, they discovered that statistics "proved" that pornography caused crime. However, the 1970 report of the President's Commission on Obscenity and Pornography, which was done by honest researchers and was highly praised for accuracy and honesty, discovered that there was "no evidence to date that exposure to explicit sexual materials plays a significant role in the causation of delinquent or criminal behavior among youths or adults."
* In the heated German censorship debate about blocking sites allegedly containing child pornography, an organization in favor of this censorship law ordered a survey at a market research institute with questions asking if the person taking the survey is against child pornography and in favor of blocking the websites containing it. Over 90% answered 'Yes'. Another survey ordered by an opposing NGO -- at the same institute no less -- used a slightly different phrasing: Do you agree with blocking the content despite the fact that this content still exists and is easily accessible after the censorship? Over 90% answered with 'No'.
* Many casinos like to advertise their slot machines with lines like "Up To 99% Payout!" to make it sound like the player has a good chance to win. First, "up to" means the payout could be 1% for all you know (although laws usually set a minimum). Secondly, even a 99% payout means that for every $100 you put in the machine, on average, you'll get $99 back, i.e. you still lose. That "99% payout" is also an average that is based on something like one million pulls (plays) on the machine. If you play 100 times in one slot machine, you're not getting a representative sample of that average. These machines work differently in the UK. UK Fun With Prizes are required by law to seek their set hold percentage within a certain number of spins (usually 10,000). To achieve this, they naturally [[TheComputerIsACheatingBastard cheat all the time]]. They also can be, and often are, programmed to go on a suck cycle and take in way more money then they need to, in order to save up for a large series of payouts later.
** A machine may have one payoff rate if you bet a single coin per spin (which most casual gamblers do) and a completely different rate if you bet the maximum coins per spin. For example, the payout for a video poker game might be $1000 for a royal flush if you bet one coin, but $7500 if you bet five. The advertised payout rates assume the player is playing maximum coins, so a casino can have a slot machine that has a completely legal and legitimate payout ratio of ''one hundred percent'' (or even ''higher'') and '''still''' manage to make money on it.
** The payoff ratio is also allowed to assume "perfect play"; that is, the person playing the machine knows to do certain things that may be counterintuitive to a casual player or even an experienced player who has not fully analyzed a particular machine's payoff values, which are frequently different even on machines of precisely the same type right next to each other in the same casino.
** Truth in Advertising laws require that if a set of machines is advertised as "Up to 99% Payout," then at least one of them must have 99% payout. Though there may very well be 50 other machines with 10% payout. Just don't expect that the "lucky" machine will be marked in any way, and since slot machines are computerized these days, most likely the lucky one changes by the day.
** The history board on roulette tables gave the illusion that the previous numbers the ball has landed in means that it should have a higher chance of landing on a number not on the board. Except the roulette table has no concept of memory and the ball has an equal chance of landing on the same number as before no matter how many plays were made. If the roulette wheel has any bias at all, it's more likely ''in favor of'' the numbers on the board -- but don't bet on it, as the gambling industry works ''very'' hard to ensure their devices are all truly random.
* A common problem encountered is Simpson's Paradox, best demonstrated by example: Suppose Hospitals 1 and 2 are nearby, but 1 is better equipped for treating people with severe injuries, so proportionally more of the people taken there are badly hurt. It does better at treating badly hurt people than hospital 2, and also does better at treating people who are not badly hurt. However, since people who're badly hurt are more likely to die than people who're not badly hurt whether or not they go to hospital 1 or hospital 2, hospital 1 may still have a higher overall death rate.\\
\\
Simpson's Paradox is when data shows one trend, but dividing it into categories shows the opposite trend. In the example above, hospital 1 has a higher death rate, but if the patients are split into categories based on severity of injury, it has a lower death rate in each category.
** The same goes with good doctors and bad doctors, as told in the book SuperFreakonomics. Good doctors are generally given tougher cases while bad doctors are given easier cases. However, if you look at death rates you see that some doctors have higher death rates, but these are usually the good doctors. Patients with serious cases are more likely to die, so good doctors lose a lot of their patients than, say the doctor who cures hiccups. The lesson is that you can be fairly certain that the doctor you receive at a hospital is competent enough to be assigned to you.
* Italy got Südtirol, which used to be a part of Austria, to be added to their territory after UsefulNotes/WorldWarII by using this kind of statistics to convince the Americans that the area was mostly populated by Italians. Which it wasn't. To this day, most of Sudtirol's population speaks German as a first language and watches German and Austrian TV, rather than the Italian channels.
* Wolf Blitzer on polling information about the health care debate in American politics:
-->We did that poll CNN Opinion Research Poll, that said, "You like this health care bill or not like it?" We just assumed, a lot of us, that the people who said they didn't like it because it was too much interference, or too much taxes or whatever. But if you take a closer look at people who didn’t like it, about 12% of those people who said they didn’t like it thought it didn't go far enough. They wanted a single-payer option, they wanted the so-called public option, they didn’t like not from the right, they didn’t like it because it wasn’t left or liberal enough. That’s how you got 50% of the American people who said, "We don’t like this plan." But only about 40 or 38% were the ones who said it was too much government interference.
* In the 2004 US Presidential Election, Dick Cheney and John Edwards stated conflicting numbers regarding the Iraq war's casualties... and both men [[https://www.factcheck.org/2004/10/cheney-edwards-mangle-facts/ were partially right!]] Skip down to "90% of the casualties".
* One statistic used to justify the creation of UsefulNotes/TheComicsCode Authority was that a large percentage of criminals liked to read comic books, ergo, comic books influenced people to become criminals. Nobody pointed out that they were using the wrong statistic - they should have been asking what percentage of regular comic book readers became criminals.
* The Victorian-era belief that masturbation could drive men insane was derived from a similar error, in that mental asylums reported frequent masturbating among inmates. The fact that mental illness can impair inhibitions ''against'' such behavior wasn't considered, nor the fact that men locked up in an asylum had few other respites from misery, frustration, or boredom. Most importantly, nobody had the nerve to ask about the masturbatory practices of men who ''weren't'' institutionalized.
* When Anthrocon decided to move from Philadelphia to Pittsburgh, one blogger who protested the move cited that only a tiny number of people would be as likely or more likely to attend Anthrocon if it moved to Pittsburgh. The organizers, however, heeded a different statistic: those that lived so far away that the move made little difference.
* [[http://www.badscience.net/ badscience.net]] occasionally shows how statistics get misused. For example, [[http://www.badscience.net/2011/10/what-if-academics-were-as-dumb-as-quacks-with-statistics/ here]] (on small samples it's quite possible that B isn't significantly different from A ''or'' C, but you can put it as "B isn't different from A, C is different from A, so we see that C is different from B", which is wrong) and [[http://www.badscience.net/2011/12/this-guardian-story-is-dodgy-traps-in-data-journalism/ here]] (limit the view to one of many multipliers which ''per se'' can't prove anything). Unsurprisingly, the areas with traditional relations to snake oil trade suffer most.
* Cryptozoology buffs are fond of citing the fact that new species of animal are still being identified with some frequency, and alleging that this means many other "hidden" species must exist under mainstream scientists' noses. They conveniently overlook the fact that most such species discovered in the last few decades are either found in isolated locales where no biologist had previously ''looked'' for new species (caves, obscure jungle canyons, deep-sea ecosystems, tiny isolationist nations), or are "found" when DNA analysis reveals that what had been considered one species is, technically, two (e.g. African forest elephants being genetically distinct from plains elephants). Not to mention the fact that the vast majority of new species are things like insects, small birds and reptiles, or deep-sea fish, animals that can easily escape notice unless someone is looking for them, and not the magnificent megafauna monsters that cryptozoologists crave.
* It was once reported that the US was using 250,000 bullets to kill a single insurgent in Afghanistan. The most major problem with this is that they conveniently forgot to explain that this included all the bullets the military was firing, even during training and weapons tests. Never mind mentioning that most bullets go toward suppressing fire or that most casualties in war are victims of bombs and artillery (which this statistic took the care to exclude). It was finally revealed that this number had used a lot of generous rounding to get to that number.
* And while we've mentioned it rounding can be used to substantially change outcomes, especially if different methods of rounding are used at different points of the calculations.
* The previous Iranian government (led by infamous president Ahmadinejad) was notorious for this. In a very obvious example, they reported the unemployment to have decreased by 50% while other independent sources suggested otherwise. Later it was discovered that they had changed the definition of an "employed" person from "one working at least 20 hours a week at a paying job" to "one working at least 2 hours a week". By removing the "paying" condition and cutting the time to one-tenth, they had managed to include people doing a variety of voluntary works and kids helping in family businesses a couple of hours each day (and still only managed to reduce the unemployment percent by 50% which goes to show how messed up their work was).
** They did the same when calculating inflation percent. When every source (from independent economists inside the country to World Bank) was reporting a point to point inflation of above 40% (as was apparent in increased prices everywhere) the official sources reported inflation of 20% or less. How did they do it? By removing some essential items like rice from item basket (the selection of items whose prices were used to calculate inflation, originally including 300 items, mostly household and food products) and adding useless items unpopular electronic devices (like house alarms) to the basket. This had a two-fold effect: 1) It removed the most popular items that naturally experience a larger increase in price during inflation and 2) Added items with little increase in their price, increasing the population without increasing the calculated inflation.
** In fact they were so bad at this that pictures of Ahmadinejad showing graphs (without title or source) and saying "I HAVE PROOF" is now a joke in Iran used to show when someone talks bullshit without evidence to back it up.
* A commonly-cited factoid about the American Revolution is that roughly 1/3 of the residents of the Thirteen Colonies favored independence from Britain, 1/3 opposed it, and 1/3 were undecided or apathetic. The comedy series ''History Bites'' (based on the premise: what if TV had been around for 5,000 years) parodied Tom Paine as a spin-doctoring pundit:
-->TOM PAINE: Only 1/3 of the colonists are opposed to independence. Now, you can't let a minority opinion like that influence public policy!
-->INTERVIEWER: But the same number are in favor of independence.
-->TOM PAINE: But now we're talking half of ''decided voters'', which is essentially a majority. You can't ignore the wishes of half of decided voters!
** The original "statistic" doesn't come from any actual poll anyway... it was an estimation made by John Adams, and he admitted he'd not done any research on that, just that it was his feeling on the matter.
* The Church Of The Flying Spaghetti Monster has semi-famously pointed out the obvious correlation between the [[http://www.venganza.org/images/spreadword/pchart1.jpg decreasing number of pirates worldwide and Global Warming.]]
* Something of a historical subversion: During UsefulNotes/WorldWarII, the Royal Air Force wanted to add more armor to their planes, but because of weight limits they needed to know which places needed the armor most. So, they examined the planes after they came back and counted how often bullet holes were found in certain areas... and then placed armor in places that showed the ''fewest'' bullet holes. This is because they'd spotted the flaw in their sample group; [[SurvivorshipBias all they had to examine was planes that had come back]]. The data did not show, as might be assumed at first glance, places that planes were most likely to be shot. It showed places that planes could be shot and ''still fly home''.
* One of the reasons France is known as [[CheeseEatingSurrenderMonkeys a bunch of cowards who can't make war]] is that its much-needed rifle update kept getting blocked by the argument "we have enough rifles". While it was true that France had a ridiculous number of infantry rifles, those numbers did not take into account that nearly all of them were flawed and most of them were ''really'' flawed. The reason it had so much was the French army was forced to try and replace the slow loading and antiquated Lebel rifle[[note]] that fed from a tubular magazine that needed to be carefully loaded one round at a time and was in practice a single-shot rifle with an 8 round emergency reserve.[[/note]] with the somewhat finicky Berthier during WWI. It was prudent to replace both of these anyway, but continuing to use them would have necessitated retaining old unreliable machine guns. When the Germans invaded, the French were caught in the middle of the slow-going replacement program and were forced to use effectively four rifles with two incompatible calibers. History can speak to the results. The lesson: statistics do not speak for quality.
* A similar scheme based on the same logic was used by Soviet admirals to ask the government for resources to build new ships. The navy would ask for X amount of money to add four cruisers to the Soviet fleet. X would always look like a reasonable amount of money for four ships so the central government would approve. [[ExactWords The navy would then bring three obsolete cruisers out of reserve and commission them]] then build one new cruiser. For the next year or so the navy would honestly say that they had four more commissioned cruisers than before and the politicians would be satisfied. Then the navy would quietly decommission the old cruisers and repeat the scheme.
* Research into PsychicPowers in the 1970s ram up against this trope when people who (by pure luck) scored well on card-guessing procedures were singled out for re-testing under closer observation. Unsurprisingly, repeated tests found their apparent "powers" didn't work at all the second time around. Had the researchers re-tested the ''entire'' population of subjects again, not just the lucky guessers, they'd have found a similar proportion of high-scoring subjects randomly distributed among the volunteers. Instead, the idea that ''being observed'' made psychic powers wane was propagated to account for the "mysterious" decline.
* {{Glurge}}-y Facebook spam will often end with something to the effect of, "Only 3% of your friends will be brave enough to share this," effectively trying to guilt the reader into helping the spam proliferate.
** Similarly, "97% of people can't solve this!" for social media "puzzles" that either have ''painfully'' obvious solutions, or to which several solutions are possible due to intentionally misleading design. Solving the puzzle proves nothing; arguing about the correct solution proves nothing; but the shares and comments feed the algorithm and thus make this sort of content [[JustForPun statistically]] more likely to show up in people's feeds.
* When reporting on the decline of a particular caribou herd in the Canadian Arctic (the Bathurst Herd), it's common for reporters and environmental groups to compare the current low numbers to the herd size in 1986 to demonstrate in how bad a shape the herd is currently in. What's never mentioned is that 1986 was the record ''high'' number for the herd count, and was 3 to 4 times the average number of animals counted in other years.
* This is commonly done when reporting the unemployment rate in the United States. At the end of Barack Obama's presidency, unemployment was officially at 4.7%. However, the word "unemployment" is defined differently in the US than what most people assume it to be. The US defines "unemployment rate" as "percentage of working-age adults (18-65) receiving unemployment compensation," typically after getting laid off (you normally don't get it if you quit or got fired). The ''assumed'' definition of unemployment, "percentage of working-age adults who don't have a paying job," is called "labor force nonparticipation" and is closer to 40%. But that figure is also misleading because it includes people who ''aren't'' looking for paying work, such as the disabled, early retirees, stay-at-home parents, full-time students, or even those that simply gave up on searching. There's also the issue of ''under''-employment, where yes Alice has a job, but it's part-time and/or minimum wage (or close to it), so she still relies on government assistance like food stamps or Medicaid. The true figure that people are looking for--the percentage of working-age adults looking for a full-time job that supports them without having to be on welfare--is difficult to pin down.
** In the US, the U-3 Unemployment Rate is the "official" rate, and is defined as persons able to work and who want to work who have sought employment within the last 4 weeks. It does not include underemployment, disability, or people who aren't looking for work. While the technical definition of the U-3 rate are well-known to anyone with the right academic training, that hardly means much to the layperson. The problem is not with the metric, but with the way it gets interpreted. The U-3 is widely understood to be a proxy measure and an incomplete picture, but it's useful because it is relatively easy to measure. As long as it is consistent, it can serve as a barometer even with known flaws. [[https://www.investopedia.com/terms/u/unemploymentrate.asp More here.]]
** Consider the above-proffered definition: "the percentage of working-age adults looking for a full-time job that supports them without having to be on welfare." As far as statistics go, it's very difficult to work with because it cannot define cases at the margin clearly.
*** First problem: what does it mean for a job to be able to support someone? The conditions for triggering public assistance in various states and nations are quite different; many nations provide public assistance even to quite well-off individuals. An objective measure such as a set poverty line might help clean up that definition into something useful. Additionally, is unemployment insurance considered "Welfare?" (Legally, it's not.) Additionally, the problem of defining "have to be on welfare." If a person could make ends meet with their full-time job and a second part-time job, do they "have to be on welfare?" What if they simply sold a house they purchased that is above their means and moved into much less expensive accommodations?
*** Second problem: working-age adults who are out of the labor force would be counted in the above definition. That definition would considered disabled, ill, or the unemployable to be unemployed even if they cannot work, as long as they want to work.
*** Third problem: a problem which also plagues the U-3 (and is well-known) is the rate would not count working-age adults who simply have given up looking for work. This is really common during an economic downturn.
*** Fourth problem: an individual who does not need to be on public assistance, even if they are looking for a job, would not be counted as unemployed. Thus, the person who doesn't have to be on welfare because they have a wealthy family, royalties/a pension, Veteran's or other disability, or just have savings to live off of while they look for work would all fail to be counted. This is a large gap, as most upper-middle-class and above workers keep a savings fund capable of sustaining them for some time if they leave their current job.
*** Fifth problem: jobs held by non-working-age persons would not count. Thus, the 70-year old physician or lawyer who still hold a job would not be counted, nor would the 17-year-old military member.
*** Sixth problem: the offered definition completely ignores the realities of part-time employment, independent contracting, and the "gig economy," concentrating only on full-time work. Someone who makes a solid living as a carpenter, consultant, or other independent contractor would not be figured into employment statistics as they move from one job to the next.
*** Seventh problem: much like the U-3, it cannot count some forms of underemployment. If a cardiologist has to wait tables to make ends meet, they are significantly underemployed.
* This is how Barack Obama came to be known as the President with the highest deportation of foreigners in the United States. The common definition of this would have people assume the number derives from "number of people who have entered the United States border and were then removed for any reason." However, the statistic also includes people who attempt to enter at border controls and are refused (technically, never entered the country). At land crossings, a person trying to enter the United States and is turned away, there is no mechanism to stop them from attempting again until they get into the country... and each one is counted as a separate "deportation" under reporting metrics.
* All the major types of car reliability statistics are likely to turn into that:
** The owner survey, listing the car owners' voices on reliability, is prone to people not wanting to admit their vehicle is unreliable, or the opposite: owners unfairly bashing their cars.
** The assistance statistics, listing how often a certain model requires roadside assistance, don't account for failures that don't immobilize the car.
** The inspection survey, listing the percentage of cars of certain age failing their inspection or the number of failures the inspector noticed on an average example of the model, don't account for failures that do not impact the inspection's outcome. Also, expensive cars are more likely to score well, because their owners are more likely to have the money required to keep them in good condition between inspections.
* Whenever people cite UsefulNotes/{{Chicago}} as being a WretchedHive due to its high number of murders, they conveniently neglect to compare that number to the city's population of 2.7 million. Crime rates for a city are measured per 100,000 residents, and while there are definitely rough parts of Chicago where crime is a major problem, [[https://en.wikipedia.org/wiki/List_of_United_States_cities_by_crime_rate it is not the most violent city in the United States]] by a long shot. It's also worth mentioning that most violent crimes involve people who know each other: gang violence, domestic violence, drug deals gone south, etc. So even if you live in St. Louis, the ''actual'' murder capital of the US, the odds of being victimized are relatively low if you're not living that kind of life.
* The vegan website [[http://veganstreet.com/ Vegan Street]] claims there's more protein in 100 calories of broccoli (11.1 grams) than in 100 calories of beef (6.4 grams). While true, there are only 31 calories in a cup of broccoli, meaning you'd have to eat over three cups of broccoli to get those 11.1 grams. Conversely, three ounces of beef contains 25 grams of protein. Furthermore, not all proteins are the same; ones from most plants don't have as many of the amino acids the human body needs.
* The Church of Scientology is unsurprisingly quite fond of this trope. The most common is ''exponentially'' overstating the number of Scientologists there are. The church often times cites membership statics based on the sale of relevant books, but there are several problems with this. Chiefly, most people who buy ''Dianetics'' don't go on to join the church, quite a lot of them are buying the book out of BileFascination, and of the ''actual'' Scientologists who buy the book, most buy multiple copies - because the organisation instructs them to, to inflate the sales numbers which are then used to justify the inflated membership numbers.
** They did the same thing with Hubbard's DoorStopper "Mission: Earth" series in the 1980s, even for the volumes that were published after Hubbard "dropped his body" (i.e., died), and did so (and still do so, from time to time) for his earlier works as well. Since all of Hubbard's writings are the intellectual property of Scientology, and thus profits from sales go into the organisations coffers, it's essentially just yet another means of squeezing money out of their members.
* Back in 1985, there was [[https://www.snopes.com/science/stats/terrorist.asp a study]] performed that showed that [[OldMaid a woman who was still single at age 40 had only a 2.6% chance of ever getting married]]. ''Newsweek'' even stated that such a woman had a better chance of getting killed by a terrorist. That study is now recognized as ''severely'' flawed, for multiple reasons. Although it ''did'' draw on US Census data from around that time, it narrowed the sample size ''far'' too much. It looked only at white, college-educated women born between the mid-1940s and mid-1950s who had never been married before, so out of 70,000 households, only about 1500 were part of the study. It also used a parametric model, which was meant for making sense of ''past'' events, not making predictions about ''future'' ones. And it did not take into consideration women who were cohabitating with partners (but not legally married); those women were counted as "single." It also didn't differentiate between those who wished to be married and those who were still single at 40 by choice (since the latter group would likely not be inclined to try for marriage after 40 either). There were also population conditions endemic to that particular generation, so even if that study ''had'' been accurate back in 1985 [[note]] Every year, the number of births increased over the previous year (hence the generation's nickname, the "Baby Boomers"), and [[GenderRarityValue there were more females than males]]. So when they grew up, the men had a lot more partners to choose from, especially as, unlike women, they didn't feel the need to wait until after they had a degree to head to the altar, and especially because many men of that time were choosing partners a few years younger than they were. [[/note]], and it wasn't, that data would be obsolete now.
* The idea that if a woman wishes to have a child, she ''has'' to do it [[MyBiologicalClockIsTicking by age 35 OR ELSE]] has been studied multiple times. Problem is, almost all of those studies are getting their data, not from a large and well-controlled sample of modern women, but census data about French peasants from TheMiddleAges! Few have ever even thought to question that data, but it's problematic for several reasons. First (and most obviously), it comes from an era before germ theory, modern medicine, fertility treatments (such as IVF), hospital births, advances in agriculture and nutrition, the feminist movement, understanding of eggs and sperm, and so much more. Secondly, it only looked at census data, which doesn't explain ''why'' few of these women were having children after 35. Sure, it ''could'' reflect that women of that time and place were going through menopause or perimenopause sooner than women today do. But it could also reflect a number of other things as well: Maybe these women had fertility problems, to begin with, many of which are now treatable. Maybe, since these were peasant farmers, they weren't getting adequate nutrition for regular ovulation or healthy pregnancies. Maybe they were dying early of diseases such as TheBlackDeath, or [[DeathByChildbirth dying in childbirth]]. Maybe sex after a certain age, in that time and place, was seen as "unseemly." Maybe their husbands had gone off to war or had died of diseases/malnutrition/etc. Maybe these women, compared to their younger counterparts, were more likely to use whatever contraceptive methods were available (or abort). We just don't know. What we ''do'' know, however, is that more ''modern'' data suggests that the decline in a woman's fertility generally happens, not in her mid-30s, but her mid-''40s''. Also, that the risk of live birth with a birth defect ''does'' double: from 0.5% to 1%. So many women can (and do) become pregnant much later in life than we've been led to believe, either with or without fertility treatments.
* [[https://www.psychologytoday.com/blog/heart-the-matter/201704/do-half-all-marriages-really-end-in-divorce The famous statistic that 50% of all US marriages end in divorce]]. This oft-cited stat came from TheSeventies and early [[TheEighties 80's]] due to more and more states implementing "no-fault divorce" laws during that time [[note]] Meaning that there doesn't need to be legal fault, such as abuse or adultery, in order to get a divorce, which spares couples who want to split amicably from having to lie to a judge, a big problem before these laws went into place[[/note]]. This is in addition women gaining far more financial independence during the same time period and no longer being trapped in unhappy marriages. But since then, the divorce rate has actually been ''declining'', as later generations feel less pressure to marry [[OldMaid before 30]] or [[ShotgunWedding due to an unplanned pregnancy]], So if a couple does tie the knot, it's because they actually want to be together rather than outside coercion. The statistic is also artificially raised by people getting married multiple times. Someone who divorces and remarries is much more likely to divorce again than someone still on their first marriage, as any Hollywood tabloid can attest.
* Often used in arguments over the UsefulNotes/AmericanCivilWar to "prove" that it couldn't be about slavery because such a small number of people had slaves (setting aside the fact that even if the small number were true, that conclusion would not follow, as most wars have been fought for the benefit of the ruling class, and the phrase "a rich man's war, and a poor man's fight" is sometimes attributed to a Confederate private). Some will try to claim a ridiculously small number of, say, 2%; that's just a straight-up falsehood. Others will get more creative, and that is where statistical manipulation comes in. They'll compare the number of individuals who own slaves in their own name to the population as a whole. Here's why that doesn't work: say a planter has a wife and three children. Of these 5 people, how many own slaves? By this measure, only one (20%), the planter himself, because all the property is in his name. In a more honest calculation that looks only at ''households'', the number of slaveowners jumps up to around 30%, and even that underestimates the economic impact of the institution, i.e., people connected to or who make use of slave labor without necessarily owning any themselves. For example: overseers, partners, and employees in corporations that owned slaves (fairly common in transportation and the building trades), the fact that slaves could be rented, etc. Sometimes, a different claim is made to back up the same line: that a slave was a very valuable commodity, often literally compared to a Cadillac or sports car, and thus could only be owned by the ultra-rich. And that's true...of a prime field hand. The fact that most slaves would not qualify as such is ignored; this is the equivalent of claiming that only the ultra-rich can own ''cars''.
* The idea that the average lifespan prior to the Industrial Revolution was only about 35 years. In actuality, ''plenty'' of people lived to be well into their 70's and older at that time, even without modern medicine. The reason the "average" lifespan was so low was because of high infant mortality. At that time, babies and children had a ''very'' high chance of [[DeathOfAChild dying before age 5]] from disease or malnutrition. But if a child made it to puberty, they had a ''fairly good'' chance of making it to old age.
* You may hear a statistic that "One in every _____ women/men will have X cancer". Usually, this is done to promote screenings for said cancer, or as part of "awareness" campaigns. What's usually not said is that this number includes ''all'' the diagnoses in that demographic, whether they survive or not, though it's often phrased as how many patients will ''die'' from that cancer.
* There's also the "survival rate." As ''Webcomic/{{xkcd}}'' [[https://xkcd.com/931/ explains]], sometimes a cancer cell or two or three will slip past whatever treatment(s) are given (e.g. chemotherapy, radiation, immunotherapy, whatever), or actually [[NightmareFuel be resistant to it]]. Which means it may come back, or the tumor may metastasize (that is, pop up elsewhere in the body). The X-year survival rate actually refers to how many years you go ''without'' this happening. (For example, if your five-year survival rate is 60%, that means there's a 60% chance your tumor ''won't'' come back or metastasize within 5 years...[[OhCrap but there's a 40% chance that it]] ''[[OhCrap will]]''.)
* Another common tactic is to try and quantify things that aren't quantifiable. For example, a shocking amount of graphs use "gun control" as an axis point. Gun regulations are laws and it's impossible to turn into numbers. Which is why the results of these graphs change so much depending on who makes them. Even trying to compare on countries regulations as "more strict" than another is a bit difficult given the idiosyncrasies of law.
** Also a factor here: Selective Enforcement. Not all laws are equally enforced all of the time - in fact, even trying to do it would probably be impossible due to the sheer number of laws on the books. Analysing the laws on the books and pegging that analysis to a numerical value does not give you any picture of how the laws in question are actually ''used'' by the Police in that area.
* One common way of making changes in numbers (say, a change in graduation rates for high school students) look worse or better, depending on the goal, is to compare the numbers in question directly without placing those numbers in context. For example, if 99 out of 100 students graduate and one drops out, and the next year 98 graduate and two drop out, while it's technically correct to say the dropout rate has doubled or increased by 100%, it's also misleading. Similarly, if you have a poor educational system where only 25 out of 100 eighteen years old have a high school diploma, and raise that to 30 out of 100, while you have increased the number of graduates by 25%, in context your graduation rate is still abysmal.
** This is also seen in reporting about almost anything that is, in context, a very small number. If, for instance, in the average year, three people in the United States come down with a rare illness, then a ''staggering 33% jump in cases''....is still a really, really small number of people.[[note]]i.e., Four. [[WritersCannotDoMath Well, technically, 3.99, but we'll overlook the rounding error.]][[/note]]
* If you look at a map of the United States where all counties are colored based on who they voted for president, using the standard red for Republicans and blue for Democrats, the map will often be [[https://twitter.com/LaraLeaTrump/status/1178030815671980032?s=20 heavily red]]. Many Republicans will use this as proof that they speak for the American people and that a handful of urban areas, which usually vote Democrat, shouldn't solely control the nation's leadership. One small problem: corn and cattle don't get a vote. Most solid red regions are rural and sparsely-populated; it may look impressive that all of West Virginia voted for Donald Trump in 2020, until you realize the entire state has fewer people than Brooklyn. [[https://twitter.com/CLTgirl98/status/1178990507663466499?s=20 A more accurate election map will only color population centers and not empty land]], in which case the red and blue will be a lot more evenly matched.
* An interesting way to skew results in your favor is to obviously make the poll biased. Most people will then vote ''against'' where they are being led either to prove that they are above control or just to spite the pollsters. The pollsters, who really are for the opposite of what they presented themselves, can then say "look people still agree with us, even when the polls are biased against us."
** People wanting to screw pollsters has an interesting effect on statistics. A common example used to demonstrate this in statistics class are maps where a certain group of people were asked to find a certain country on the map. Ten to Twenty percent will point to somewhere ''in the middle of the ocean.'' Teachers point out that while it's ''possible'' for people to be that stupid, it's far more likely that was their idea of a joke.
* Many studies have shown that [[AllMenArePerverts men are more likely to develop sexual fetishes than women]], and no one really knows why. However, most studies involving human sexuality rely on self-reporting, and it's possible that men are more likely to be ''honest'' about their kinks and explore them more actively, whereas a woman might downplay hers, due to society being more encouraging of men being openly sexual while shaming women [[DoubleStandard for the same thing]]. The same thing applies to number of sexual partners; when men and women self-report on them, it's a common adage to either divide the number by 3 or multiply by 3 depending on whether it's a man or a woman making the claim.
* UsefulNotes/VladimirLenin used a form of this in his propaganda book ''Imperialism'', written to bolster Creator/KarlMarx's work for the 20th century after 70-odd years of contrary evidence to his prediction of "hypercapitalism", by suggesting that western capitalist economies had staved off inevitable collapse due to exporting surplus capital to their colonies, therefore making the "class struggle" global. As Thomas Sowell pointed out, aside from Lenin not citing his sources, the categories he used to illustrate his argument ("Europe", "Africa-Asia-Oceania", and "Americas") lumped together so many disparate economies under the same rubric that the data was completely useless at proving anything. In reality, the industrialized economies primarily invested in each other, not their colonies, which only further dismantles communist theory of an ever-increasing capital concentration leading to revolution.
* The Soviet Union was infamous for overstating their accomplishments by running figures through arbitrary multipliers and claiming that is simply how Marxist-Leninist economics worked. Some of the more infamous examples:
** The Soviets would add the value of components to finished products, and then the finished products themselves, to their GDP. Normally everybody only tabulates the finished product, but a screw driver made in the Soviet Union would have the handle, the shaft, and the screwdriver itself counted toward the Soviet GDP.
** The exchange rate between the Rubel foreign currencies was eventually completely arbitrary, again making the economy look stronger than it was. For decades, the exchange rate of dollars to rubles was 3.75 dollars to the Rubel. This figure did not change despite the dollar experiencing inflation and the Rubel officially not. In reality a single dollar often went for more than eleven rubles on the black market, and in terms of comparative purchasing power in their native markets, the dollar was usually somewhat stronger, but not nearly so much as the black market price.
** The USSR would often undergo a major drive to pump up some statistic, and, once they got to a number the liked, simply officially set the statistic as that number and never do any follow up studies. Sometimes not even the initial study would be empirical, with a bunch of experts pressured to come up with a flattering result tasked to reason what they thought the numbers were with little to no data. One of the most infamous cases was the nation's homeless statistics, where the party simply declared that their homeless population was zero and forbid even themselves do do any further research into the issue despite homelessness continuing to exist.
** They would often inflated production numbers by listing product that had been stolen, spoiled, gone deflective, or never existed. [[GoneHorriblyRight Which backfired tremendously on the Soviets.]] It turns out the workers effectively did the same thing as the government by cheating on their own production numbers, thus meaning the government had to ''pay'' for material that was either bad, stolen, or never existed at all. The most famous incident was the Uzbikistani "Great Cotton Scandal," which revealed that essentially the entire Uzbek Socialist Republic was in on scamming the central government into paying for fictional cotton.
* The old adage that you should drink eight glasses of water per day is misleading. The reality is that a healthy adult should consume a total of about 64 ounces or two liters of water in a day, but this includes the moisture in the foods you eat; you don't have to chug away. In fact, if you're eating three square meals each day, you can survive without drinking ''any'' additional water. It's not recommended (unless you're a desert tortoise), but you won't die.
* In 2019, a study was publicized by the media for how many shootings had occurred in a matter of months in the United States to demonstrate a problem with gun culture. When you read the study, they state that they defined a shooting as any event where a firearm was reported even if it was not used, anyone was injured/killed, or was even present at all. Another study reporting the number of children killed by guns in the home defined a child as a dependent under the age of 25.
* Sometimes smaller countries will compare negative statistics, like crime rates, directly to larger countries. Without dividing by population, this will invariably make the smaller country look better. This is why serious studies use "per capita" rather than absolute statistics.
* Cases where a population that's a minority within a country is vastly overrepresented in its prison system. Depending on one's world view, they can be used to back an existing belief that members of the minority are inherently more prone to committing crimes, that members of the minority are more likely to find themselves in circumstances in which they are driven to crime to survive or that the justice system is being much harder on members of the minority than on those of populations that are underrepresented in the prison system. Multivariate analyses of such trends are rarely forwarded because they do not fit any side's particular narrative.
* In an open letter to left leaning parties, UsefulNotes/TonyBlair noted how such parties often convince themselves that victory is assured because some of their flagship issues poll well. Essentially, just because, for example, spending more money on the NHS, repairing roads, and aid to the homeless all poll at sixty percent, doesn't necessarily mean it's the ''same'' sixty percent of the populace. After that, a specified proposal to solve an issue has less support than simply solving the issue. Then there are voters who will be turned away by your party's general attitudes about society and even more when a face is finally put to all these ideas. Yet every major left-leaning party seems to be shocked when they lose because they convince themselves their candidates are wildly popular just because the voter base broadly agrees with them on some of the issues.
* Inverted when Darrell Huff was hired by the tobacco industry to apply ''How To Lie With Statistics'' to the stats showing pretty clearly that smoking causes cancer.
[[/folder]]

* The Victorian-era belief that masturbation could drive men insane was derived from a similar error, in that mental asylums reported frequent [[ADateWithRosiePalms Dates With Rosie Palms]] among inmates. The fact that mental illness can impair inhibitions ''against'' such behavior wasn't considered, nor the fact that men locked up in an asylum had few other respites from misery, frustration, or boredom. Most importantly, nobody had the nerve to ask about the masturbatory practices of men who ''weren't'' institutionalized.

** This is also seen in reporting about almost anything that is, in context, a very small number. If, for instance, in the average year, old three people in the United States come down with a rare illness, a 33% jump in incidence....is still a really, really small number of people.

* The Church of Scientology is unsurprisingly quite fond of this trope. The most common is ''exponentially'' overstating the number of Scientologists there are. The church often times cites membership statics based on the sale of relevant books, but there are several problems with this. Chiefly, most people who buy ''Dianetics'' don't go on to join the church, quite a lot of them are buying the book out of BileFascination, and of the ''actual'' Scientologists who buy the book, most buy multiple copies.

* Pretty much the oldest trick in statistics is implying that because X is increasing/decreasing and Y is doing the same or the opposite, then X must be affecting Y. In reality, this isn't necessarily so unless you can manipulate one of the variables. Without other evidence Y could be causing X, or they could both be caused by Z. For example if someone presents a graph that shows street violence is increasing as video games are getting more violent,[[note]]Which isn't true but let's pretend[[/note]] they want you to think that violent games are causing street violence, but you can easily come to the conclusion that video games are becoming more violent as a reflection of a more violent society.

* This is how Barack Obama came to be known as the President with the highest deportation of foreigners in the United States. The common definition of this would have people assume the number derives from "number of people who have entered the United States border and were then removed for any reason." However, the statistic also includes people who attempt to enter at border controls and are refused (technically, never entered the country). At land crossings, a person trying to enter the United States and is turned away, there is no mechanism to stop them from attempting again until they get into the country... and each one is counted as a separate "deportation" underreporting metrics.

* This is how Barack Obama came to be known as the President with the highest deportation of foreigners in the United States. The common definition of this would have people assume the number derives from "number of people who have entered the United States border and were then removed for any reason." However, the statistic also includes people who attempt to enter at border controls and are refused (technically, never entered the country). At land crossings, a person trying to enter the United States and is turned away, there is no mechanism to stop them from attempting again until they get into the country... and each one is counted as a separate "deportation" under-reporting metrics.

** For an example of X and Y being manipulated by Z, the number of pipes burst and the number of sweaters worn both go up as a result of freezing temperatures. Someone who hates sweaters could take this information and make is into a graph of pipes broken versus sweaters worn, and from that data alone it would appear wearing sweaters causes pipes to burst.

** They would often inflated production numbers by listing product that had been stolen, spoiled, gone deflective, or never existed. [[GoneHorriblyRight Which backfired tremendously on the Soviets.]] It turns out the workers effectively did the same thing as the government by cheating on their own production numbers, thus meaning the government had to ''pay'' for material that was either bad, stolen, or never existed at all. The most famous incident was the Uzbikistani "Great Cotton Scandal," which revealed that essentially the entire Uzbek Socialist Republic was in on scamming the central govenment into paying for fictional cotton.

In the end, statistics are not lies and statistics don't lie: people lie about the statistic itself or how it is interpreted. Some don't lie, they are simply ignorant, as are most members of the public in terms of statistical interpretation. See LogicalFallacies and CriticalResearchFailure.

* Whenever people cite UsefulNotes/{{Chicago}} as being a WretchedHive due to its high number of murders, they conveniently neglect to compare that number to the city's population of 2.7 million. Crime rates for a city are measured per 100,000 residents, and while there are definitely rough parts of Chicago where crime is a major problem, [[https://en.wikipedia.org/wiki/List_of_United_States_cities_by_crime_rate it is not the most violent city in the United States]] by a long shot. It's also worth mentioning that most violent crimes involve people who know each other: gang violence, domestic violence, drugs, etc. So even if you live in St. Louis, the ''actual'' murder capital of the US, the odds of being victimized are relatively low if you're not living that kind of life.