The fact that the statement “most statistics don't actually tell most people anything interesting about the world” relies on two statistical conditions is humorous to me.

Statistics aren’t bad. Extrapolating unwarranted conclusions is bad.

I am not a trained medical professional, and I don't have time to be scrutinizing and reading background literature to gain a full understanding of the context each specific study has within its sub-field. I don't have access to whether these conclusions are generally accepted or are commonly disregarded for some technicality in the methodology that I didn't understand. Me personally, I can't be an expert in everything all the time, and I can't waste my life gaining a full breadth of understanding of every topic. I rely on extrapolations to do this for me, if those extrapolations are commonly faulty, I won't pay any mind to them.

Unfortunately, this is the best option for modeling the world around us that humanity has come up with. Throwing out the only tool we have to evaluate and re-evaluate what we consider to be knowledge is not the best course of action.

Solution is to support proper scientific procedures, support civic organizations carrying out the important tasks of vetting claims, and being an example of someone who values the correct protocols when evaluating data and making conclusions (or not making them).

It's a very slow process, and we're lucky we're in a timeline where the work of various people throughout history has gotten us here. We'll have to work to keep it going further, and literacy in math, statistics, and the scientific process is crucial.

I disagree here strongly. It is not the best option. Statistics has become the tool of obfuscation and making exaggerated claims.

Compare the two statements: "10 out of 80 people that took the drug lived a month longer but were extremely miserable."

Versus: "A new cancer drug doubles the average survival rates scientists say".

Which one do you understand better? Here I just made up some examples, but as a scientist I'll tell you just about all the papers I read are in the second category, it takes an unreasonable amount of effort to get the first type of infomration out of the publications.

I don't understand the chain of reasoning in your claim regarding the 1st and 2nd statements, and statistics in general.

When I use the word statistics in this conversation, I mean the use of math to analyze and compare data. This can be done inappropriately, of course, but the problem doesn't lie with the principles of math (including statistics), but the agent using them inappropriately.

And medical (or biology related) statistics are used most inappropriately, due to various cognitive biases and financial incentives. But the problem is not statistics, and it's still the best tool we have unless we can find a Magic School Bus to shrink us down and go observe the chemical reactions in real time. But I know that stats are abused, so anytime I see a fantastical claim, especially in medicine, I approach it much skepticism.

There are fields of science that have banned certain statistical measures:

Psychology journal bans P values

https://www.nature.com/news/psychology-journal-bans-p-values...

I happen to agree with the sentiment expressed above that the entire concept of hypothesis testing is fundamentally flawed. This is what you call the "best tool". It is not, it is the tool that is used because it allows for the most fudging of results.

the problem is that in many (most cases?) even trained scientist would have a hard time deciding which statements are statistically valid.

There are endless ways to be unintentionally obscure, to semi-intentionally obfuscate or to just flat out lie. And it very hard to tell these apart - one would need to dedicate more effort than usually worth.

That's why we have organizations like the CDC, FDA, NHTSA, etc. We have the internet, cheap computing and storage of data, so it should all be transparent. Countries verifying claims independently goes a long way too.

And obviously (sweeping) results from health related experiments should obviously be met with skepticism considering the complex nature of the problem.

> Extrapolating unwarranted conclusions is bad.

But that's practically all they're used for!

Not in physics or chemistry. It's a bigger problem in biology because we want to be able to make claims but don't have the means to properly design and carry out experiments that aren't subject to many errors.

Sure, but is it significantly bad?

Wouldn’t knowing statistics allow the average Joe to realise that most of these studies are simply not representative?

That a current generation is under-equipped to use quantitative discourse is sad, but it also suggests that what communities need are experts and not evidence. They want to listen to a trustworthy leader and not conflicting data -- because data will conflict.

> but it also suggests that what communities need are experts and not evidence

IMHO it actually suggests we need to improve the average person's educational level

I think this is missing the point though, I've taken an upper level Statistics class, most people here with CS degrees have, but that doesn't help me understand whether a sociological study has a faulty component in its methodology. This is compounded by the culture of not reporting full data alongside papers, and the culture of writing papers in terse language.

no, unfortunately, it is not about lack of education

walk into any university, go from one faculty office to another, ask each professor what a p-value is and the overwhelming majority won't give you even a half-decent definition (I did this and can attest to it)

you know why? because p-values are far more complex than people care to admit. Most statisticians cannot properly use it! There are ongoing "ideological wars" over what it actually means. Even the founders Fisher etc argued all the time about them. If you used p-value correctly, you would almost never be able to find anything, not even valid results.

So people have to "bend" the truth a little bit. Nowadays more often than before, the truth gets bent out of shape.

If people were better educated regarding math and statistics, then it would become much less effective to "bend" the truth, especially with verification of experiments.

But yes, society doesn't function well when people are corrupted.

you have to bend the truth because it would not work otherwise at all.

Your distribution is not normal - it almost never is, now you have to argue that the method is still robust - there is no proof that is indeed robust as you claim. Then your measurement is a bit biased, see on Tuesdays the measurements do turn out a little bit differently, but most often does not matter, so you skimp over that as well.

Statistics is the wrong approach because if you did everything correctly and stuck to the letter of it you only end up hurting yourself. You'll make fewer of the valid discoveries that you make when you bend it a bit.

This is the fundamentally wrong thing about it! Strictly sticking to the rules will make you weaker. Bending statistics a little leads to more valid results.

I don’t understand. Using statistics doesn’t imply using normal distributions.

I just gave you an example, there are many statistical measures that have that particular requirement.

If it is not normality then something else. Every statistical measure makes assumptions, most of which never quite hold true. That was my point. When someone uses statistics they always fudge things a bit. Many times does not matter, if you were stickler for "precision" it would not work.

To understand statistics, you have to either know some statistics, or be willing to learn.

It is it's own thing, the art and science of making data and observations into information. Yes newspapers and the regular pundits abuse it all the time, but don't pin that on statistics, pin that on those that abuse it with, or without (usually) knowing better.

Statistics are unavoidable, unavoidable enough that much of our perception seem to make use of the same statistical calculations sciententists use, only without us having the ability to scrutinize its workings.

A statement like "most statistics don't actually tell most people anything" demonstrates both a profound lack of understanding of statistics. So why comment?

As the old saying goes: "Better to remain silent and be thought a fool than to speak and to remove all doubt."

Bet you dollars to donuts none of the underlying studies you complain about pass judgment in the form of x good x now bad. Those are always added by the pop media or implied by the reading.

There's an interesting blog post about something similar:

https://slatestarcodex.com/2014/04/28/the-control-group-is-o...

Basically it boils down to the fact that studies & statistics used are still very, very, prone to human bias.

Which can as well be seen an argument why statistics are necessary, as even with them we are stuck with a lot of bias. Imagine where we would be if we didn't have statistics at all!