The other 38% didn't understand the question...(my extrapolation)
I did a couple semesters of statistics at uni. And I can confidently say that the number of people who can answer 3 simple questions on statistics (like say mean versus medians, confidence levels or margins of error) is, well, a rounding error from 0.
Indeed, statistically, no-one has a clue how statistics work.
I did however learn enough to know that statistics can tell you absolutely anything you want them to say. Assuming you don't just make them up, they're trivial to manipulate to generate the headline you want.
When used to evaluate risk, the comprehension goes down further (a fact willfully exploited by any decent marketing.)
Statistically, most statistics are meaningless.
well encapsulated in the quote popularized by Mark Twain "Lies, damned lies, and statistics" [1]
[1] https://en.wikipedia.org/wiki/Lies,_damned_lies,_and_statist...
One of my favourite jokes: 93 % of statistics are made up on the spot, and 61 % of people believe them. Bonus points for changing the figures every time you retell the joke.
A statistician is a man who with his head in the freezer and his feet in the oven can say "On the whole I feel perfectly normal."
Recommended reading: 'How to lie with statistics' - Huff, 1954
:)
Our stat prof was a very special guy.
If even one person has 1 leg, then the average is strictly less than 2.
I’m increasingly convinced this is a thought-terminating cliche. Understanding the difference between a median, mean and mode is fundamentally empowering. Enough people, however, will stop themselves from trying to understand that by quoting such a joke.
High-quality statistics allow themselves to be inspected. When a statistic is being passed around unadorned, particularly with a call to action, that’s no different from an uncited fact being accepted at face value. (Most of the complexity of statistics is in sampling. Most of the errors in statistical judgment tend to come down to misunderstanding how a sample’s statistics translate to the population you actually care about.)
It's basic when you are attending an undergraduate course but most people can understand mean (as a dictionary might generically define it) and have a general feeling for margin of error (again, not in the mathematical way.)
Statistics has been the most difficult course in my CS course. For some reason when I start counting events to get a probability I find several perfectly plausible ways to count them, get five different probabilities and none of them is the correct answer.
And about being "trivial to manipulate [numbers] to generate the headline you want" a politician once told me that you can show the same numbers in any way you want, as in to demonstrate a thesis or its opposite.
Meanwhile 6-sided dice don't map to nice numbers, and having two dice makes the maths even harder to intuit
This isn't my original thought, by the way. Someone wrote more about it here: https://towardsdatascience.com/humans-are-bad-at-probability...
Everybody has them, and they're not useless. Usually they're regularly updated when new information comes in.
Nowadays I often stumble upon this or that statistical topic, watch an educational video, read a wiki explanation and it makes sense. Plus I've picked up unstructured and chaotically a lot of terms just by reading IT articles and forums.
tl;dr - my point is, our education is severely lacking a simple, short and concise statistics course on ELI5 level, for middle schoolers. And it is a huge gap in skills people actually do need in common life, outside of STEM. And the skills I'm talking about aren't even hard, a core set of basic concepts, without any math, can likely fit in a tiny brochure written in simple literary English with a few illustrations. Or a set of YT videos along the same lines.
I've been trying to talk about the median vs. mean with a bunch of politicians on themes around the zillionaires, wealth or consumption and for most parts they're clueless. Or the ones with degrees still go with the normal (mean that skews the normal) as they're afraid.
I just happened to get a copy of naked statistics yesterday, as I feel the human traits of poor comprehension of probabilities can be enhanced to at least some extent. And my degree from the social side didn't include statistics.
If there are better entry-level books on the matter I'm happy to take some recommendations.
I was recently convinced by a rather compelling argument that technocracy (rule by the technically capable) often falls to a local minima where the people in power find the first scrap of evidence that supports their preconceived agenda. Wildly racist? Natural selection “proves” that all of your decisions are “technically” justified.
But more directly to your point about being effective communicators of statistics, I’ve found that it’s difficult to help someone form their own conclusion from statistical results. Most people want the conclusion fed to them, with the statistics supplied as evidence. It’s up to us (proper statisticians) to act ethically: provide analyses in good faith and challenge the poorly conceived analyses of amateurs and statisticians acting in bad faith.
In the university I went to, Department of Statistics used to be part of the Faculty of Social Sciences. It was impossible to graduate from social sciences without taking a few classes of statistics. Then in some reorganization, Department of Statistics merged with Department of Mathematics. Except that old-school statisticians didn't want to move to the Faculty of Science, and they somehow managed to keep their offices in the Faculty of Social Sciences.
Dunno really.
That's bonkers. Society is all about statistics, and the reverse. Without statistics any kind of social studies is only Just So stories.
Rhetoric teaches how to argue with words. Statistics is the same, but with numbers.
Rhetoric is persuasion under the presupposition that what you are trying to persuade someone of is true. Sophistry is indifferent to the truth and is merely concerned with results. Rhetoric respects the humanity of the interlocutor. Sophistry seeks to exploit him.
When told to a statistically illiterate person who isn't aware of Simpson's paradox and so on?
what happens when data just russian-dolls in both directions the deeper you look?
>A common example of Simpson's paradox involves the batting averages of players in professional baseball. It is possible for one player to have a higher batting average than another player each year for a number of years, but to have a lower batting average across all of those years. This phenomenon can occur when there are large differences in the number of at bats between the years.
The per-year values aren't weighted in the combined total average.
Which is a rounding error from 100% according to the GP.
Why not 5% of Americans having the same average level of understanding and only 47.5% having a lower than average understanding?
What assumptions are you making, and why should they be true?
Average and mean are the same: normal distribution. Average higher than mean: skewed by big outliers.
Professional statistician affects the distribution of statistics knowledge like billionaires affect wealth distribution.
Again, why does the GP assume that a full half are distinctly lower than "average understanding" ? Why the assumption that multiple numbers cannot share an average understanding?
Your suggestion suggests that a little bit of technical education would change political allegiance. Alas that is incorrect. Politics is about worldview (me and mine versus you and yours) and a world view cannot be changed by something as mundane as education.
Each person has both a self-centered side, and a community-centered side. For some it's mostly self centered. For others mostly community centered. Politics is about finding out which side the population has swung to.
And as you see how reality and society tend to 'really' work, wisdom in other words, it tends to result in a shifting perspective on the ideal way forward for society. I'm certainly much more socially minded than when I was younger now. And I also think that's fairly typical - like most young people I was completely self absorbed which makes 'real' social mindedness quite uncommon. Yet my political leaning is gradually shifting ever more towards the 'self centered' orientation, in your terminology.
- Interpret and critically evaluate statistical representations from the media and the local community.
- Calculate measures of central tendency and measures of dispersion in custom and real datasets, and use the results to describe the data.
- Calculate and evaluate probability in statistics and games.
This came after my time, and our PIRSA score aren't the best so perhaps practice is lacking.
[1]: https://www.udir.no/lk20/mat01-06/kompetansemaal-og-vurderin...
There's a reason actuaries get paid the big bucks.
This is a tests of knowledge of definitions than actual statistical knowledge.
Combined with Dunning-Kruger, this means that the real number of people completely clueless about statistics is closer to 38%.
(Also, I assume you are joking in how you apply the effect here even assuming it is real)
[0] https://pmc.ncbi.nlm.nih.gov/articles/PMC8992690/
[1] https://www.sciencedirect.com/science/article/abs/pii/S01602...
His reply was that 10% was nothing, that means you've got 90% chance to survive. Then said you've probably got a 10% chance of being hit by a bus every time you cross the road. Just absolutely no idea what probabilities are at all or ability to extrapolate probabilities from real world lived experience.
Ask him how many times he would roll that dice
No other question matters. There are some risks but the key element is the understanding of the statistical variance because that allows me to say that "ok 50% of P can afford to be down for 3 years and I won't be homeless".
Most people don't know this but most money managers(managing money for ordinary Americans) that you hire compute this number _once_ and make tiny adjustments to your portfolio(I am talking once a year maybe) and take 1-2%.
I follow this guy called Dave Stein who has a B2C product called money for the rest of us that taught me this. I am not affiliated with them in any way.
But I found This is a real practical everyday example of why statistical uncertainty is important to know about. It teaches you how to compare — risk adjust — two vastly different investments e.g. BTC vs S&P or indexing vs value strategies. Then you sleep at night.
It is also not intuitive and many people are very anxious and FOMO driven when it comes to money. So you need to internalize the idea for it to sink.
The drawdowns are a statistical measure and you could be unlucky to catch a big depression style thing once in 30 years. And T is typically longer than 5 years, typically 10 or more.
Given how effective stories and anecdotes are in convincing people, it really seems like the human brain is not wired to grasp these concepts easily.
also, probability theory vs statistics is an important distinction: prob theory is a nice clean mathematical subject, while statistics is almost the philosophy of applying probability theory to the world.
By the time I took my statistics class in undergrad math, coming from a similar background to you, they just sort of assumed you had a head for combinatorics and used that to develop everything else. I was a really good student in undergrad and statistics was my hardest class, I spent like 2x time on that class than any other class.
In grad school I took a class on complex system failure analysis and was quite apprehensive. My hope was that I could team up with a classmate to help with the math while I could work on the systems levels analysis. Turns out that because I understood systems really well, system failure offered me the intuition I needed to really understand statistics. I aced the class, published a moderately popular paper in distributed systems using what I learned, then went and took our graduate level statistics class widely known to be very difficult and aced it.
I think tacking statistical thinking on as an afterthought in curriculum is a huge mistake in the school system, especially so in the age of machine learning. I think for the average student statistical thinking is even more important than a lot of trigonometry.
It is not just journalists and politicians that are guilty of that. I see that from many academics who should know better, particularly in humanities departments.
Many people selectively shop around statistics that match their prejudice or their social circles social etiquettes.
Yeaaah… let’s talk about that.
Seems like a fair bit of stats were designed to intimidate —so as to get people to stop asking questions. Or at least that is the effect!
Stats designed for intuition are few. See Kill Math for how it might be done: https://worrydream.com/KillMath/
Yes, stats can be used deceptively or incompetently used, but they are generally useful. You just have to look at the full study to see what the starting data was and all the actions taken on it. Then you have to make a judgements call on if/how you want to apply that to your own choices, political policies, etc.
A great example of a stats being used by two sides of an argument and political positions would be gun control. See Everytown Research and Crime Prevention Reseach Center. You can see what gets measured, included, excluded, etc by each side and how those results get fitted for support of political policy.
100% of headlines of statistics-related articles must follow this rule.
The first question was: “How much do you understand about statistics and p-values?”
not probability, p-values.
I had to look up p-values to check it meant what I thought it did to avoid making a fool of myself in this comment. I knew I was probably right, but possibly wrong.
The same goes for my assumption that this survey was engineered to deliver a bad result. Why? because it makes it easier to get funding for a the author's solution to the problem. Again, probably right, possibly wrong.
Statistics, like math, is a language, first learn the vocabulary, then you can judge the meaning
If the figures are biased, just estimate the bias and correct for that, what is the big deal they said, as if the bias variance tradeoff was not a thing.
Like, if I asked the average person here in the UK what a p-value was, I suspect the majority either wouldn't know or would have barely heard of the idea. I suspect those numbers wouldn't change much in other countries, whether in Europe, Asia, Africa or South America.
You can probably lead a decent life without understanding kurtosis and skewness. You probably can’t if you don’t understand when a normal distribution applies.
I suspect that it’s far far less than 38% of people who actually understand statistics to this level. I suspect if someone on HN went around and asked their co workers to explain what a P value is in 2 sentences, it would be less than 10% of a (presumably) highly educated workforce.
I suspect about 40% of adults are unable to tell the difference between mean/median/ mode, or could answer the Monty hall problem, or even “if I flip a coin 3 times are the chances I get heads 3 times in a row”
I use p-values daily. I can and do compute them using various methods, including by hand. But I'd still not be confident in my two sentence explanation. P-values are very unintuitive and very easy to get subtly wrong.
Other than that choice of example, I do agree in that I doubt anywhere near 40% of adults have basic statistical literacy. I've played in card game tournaments semi-professionally and just gambler's fallacy + results-oriented thinking alone make it so easy to take other people's money, and if you can't figure out such basic concepts as "getting tails once doesn't mean I'm due for a heads next flip" even when you're literally losing money, what are the chances of anyone else caring about understanding it when they're not even being given the hands-on reward-based reinforcement learning opportunity?
No surprise here.
There's a big ego hit in admitting you don't know something. And many people are brought up thinking that it's a shame not to know something and that someone is better for knowing something. Like, a better person, not just better in some field.
So, when surveyed, they tell they are in the other half than they really are?
This problem with science is apparent in another survey: in 2009, a Pew Center publication showed that 33% of scientists in the USA believed in God (and 18% in a transient power), which is much lower than the 80% belief of the general American population at the time. Of course, this is not a proof of causality in either direction, but scientific knowledge is seemingly inversely correlated to religiosity. And the USA are still more religious than any other industrial more-or-less-democratic country.
But if they release headline “62% of respondents” reported no or limited statistical knowledge while only 11% regularly use statistics in daily life…
…then they would loose more than half of readers who don’t know “per cent” or % symbol (?)
I thought that OP changed the original headline but no, psu.edu really published this :)
I have seen too many parents (i am a teacher) lacking basic mathematical and geometrical understanding.
Let alone, statistics.
Schooling is broken, society is rotting, culture is dead.
"You know how dumb an average American is. Well, mathematically speaking, half of them are even dumber than that."
You're kidding, right? 38% self-report more than that? If their self-report were accurate it would imply an education system that has truly excelled.
Now: the p-value is the probability of you getting a result at least this extreme assuming the null hypothesis (that there is no difference in average height between the general population of French and Swedish people).
Low p-value basically means how surprising your data would be if there were actually no effect (ie less than 5% of the time you’ll get this due to randomness if there is no change — which is rather impossible)
Sample size matters heavily. With more observations, estimates become more precise, so increasingly small differences can become statistically significant. With a large sample, you can therefore get a tiny, practically meaningless effect with a very small p-value.
Eg effect of $1 can be statistically significant (not random) which does not matter in practical terms if average is like $10000.
So the key point here is not only to look at the p-value but also at an actual change. If a drug gives you only 0.01% more hair, it doesn’t matter to you that it is guaranteed.
Similar things are true of Bayesian stats, leading to things like predictively oriented posteriors being studied nowadays.
I’m also pretty sure I would fall in the camp of saying “nope don’t understand P values” as I can’t remember anything else about them.
So I admit I only know that P values are somewhat useful some of the time.
A p-value is the probability, assuming the null hypothesis is true, of obtaining a result at least as extreme as the one actually observed.
Put differently: if the null hypothesis were true, then for p=0.05 you'd see <things at least as far from the test statistic as what you just observed> at most 5% of the time.
Put differently again: If the null hypothesis you are testing is true, then for p=0.05 random sampling would not return an observation as far from the test statistic as you just observed, 95% of the time.
Is that a well designed survey question?
I would expect the use of a specific jargon term in that question to affect the results in a significant way.
On a somewhat related note, 8% of Americans say they can beat a gorilla in a fist-fight.
More than half would not be able to answer this, and not only in US.
Sure on the surface stuffile averages, medians, standard deviations etc are quite easy but the moment you start going deeper you realize how difficult and un-intuitive things start to get.
I'm still in the valley of despair, and frankly it's probably the best place to be in for the average person. Understand the basics and know just enough to realize how easy it is to mislead people with it.
Just like how those college kids saw my work as horrifically weird hard work, while I saw it as a normal thing.
For example, if OP is a coal miner that hasn't had health issues yet, they may choose to discount statistics that declare x% of coal miners have negative health outcomes.