Slashdot Mirror
Is Statistical Significance Significant? (npr.org)
More than 850 scientists and statisticians told the authors of a Nature commentary that they are endorsing an idea to ban "statistical significance." Critics say that declaring a result to be statistically significant or not essentially forces complicated questions to be answered as true or false. "The world is much more uncertain than that," says Nicoole Lazar, a professor of statistics at the University of Georgia. An entire issue of the journal The American Statistician is devoted to this question, with 43 articles and a 17,500-word editorial that Lazar co-authored.
"In the early 20th century, the father of statistics, R.A. Fisher, developed a test of significance," reports NPR. "It involves a variable called the p-value, that he intended to be a guide for judging results. Over the years, scientists have warped that idea beyond all recognition, creating an arbitrary threshold for the p-value, typically 0.05, and they use that to declare whether a scientific result is significant or not. Slashdot reader apoc.famine writes: In a nutshell, what the statisticians are recommending is that we embrace uncertainty, quantify it, and discuss it, rather than set arbitrary measures for when studies are worth publishing. This way research which appears interesting but which doesn't hit that magical p == 0.05 can be published and discussed, and scientists won't feel pressured to p-hack.
"In the early 20th century, the father of statistics, R.A. Fisher, developed a test of significance," reports NPR. "It involves a variable called the p-value, that he intended to be a guide for judging results. Over the years, scientists have warped that idea beyond all recognition, creating an arbitrary threshold for the p-value, typically 0.05, and they use that to declare whether a scientific result is significant or not. Slashdot reader apoc.famine writes: In a nutshell, what the statisticians are recommending is that we embrace uncertainty, quantify it, and discuss it, rather than set arbitrary measures for when studies are worth publishing. This way research which appears interesting but which doesn't hit that magical p == 0.05 can be published and discussed, and scientists won't feel pressured to p-hack.
But not always.
They have bad data and are tired of removing outliers and getting called on it. Laziness.
Mark Twain knew this a century ago and actually said it. This is not news. I can only conclude that by, 'scientists', they mean, 'millennials'. It's called disconnecting from the hive and thinking for yourself, it's hard, I know (it isn't really that hard).
If there isn't some kind of cutoff-point to determine if an effect is real, how will we determine if it's real? The answer is that we'll be forced to fall back on rhetoric. If you want to see how far that kind of reasoning can take humanity, look at the thousands and thousands of years of almost-stagnation during the enlightenment.
The contention that this group's argument starts with is that if some effect exists, then it can be measured, but failure to figure out how to measure it doesn't mean it isn't real - sure, we should accept that, but it's trivial! The effect might be real, but if we can't figure out how to measure it, it may as well not exist for how much it can inform the progress of human knowledge. Any scientist unable to recognise this is not a scientist, no matter their degree.
Then I took a course on statistics, and the stats professor told me that 47.37% of all statisticians make up their own statistics.
Some drink at the fountain of knowledge. Others just gargle.
100% of all published incorrect results have a P value above 0.05
Some drink at the fountain of knowledge. Others just gargle.
> 850 scientists and statisticians
Not a statistically significant representation of the scientific community.
A prime number is divisible only by itself and 1
1 is prime (by this definition)
3 is prime
5 is prime
7 is prime
11 is prime
13 is prime
9 is experimental error.
The proposition that "all odd numbers are prime" has a P value above 0.05.
Some drink at the fountain of knowledge. Others just gargle.
If the goal of a field is to develop an understanding of what's actually going on in a given domain, publishing even more marginal/unreliable results is counterproductive. The signal-to-noise ratio of the scientific literature is already getting worse by the minute....
I picked a really bad time to stop shooting up heroin.
Nope. I'll delete it from Wikipedia later today.
*BSD is dying
The Curfeu tolls the Knell of parting Day,
The lowing Herd winds slowly o'er the Lea,
The Plow-man homeward plods his weary Way,
And leaves the World to Darkness, and to me.
*BSD is dying
Now fades the glimmering Landscape on the Sight,
And all the Air a solemn Stillness holds;
Save where the Beetle wheels his droning Flight,
And drowsy Tinklings lull the distant Folds.
Save that from yonder Ivy-mantled Tow'r
*BSD is dying
882: Significant
If intelligent life is too complex to evolve on its own, who designed God?
https://xkcd.com/882/
In particle physics, (the field in which I have my Ph.D. but--full disclosure--no longer work), the standard is 3 sigma to claim evidence for an effect, and 5 sigma to claim discovery. Publication of results below 3 sigma is not only encouraged, but required...it's unethical to conceal such results. A null result can be a theory killer.
this will lead to as many definitions of statistically significant as there are papers. At least know you have to hack a specific statistic if you want to cheat, and this can be spotted relatively easily. Compare that with the inevitable claim "this is significant because I say so" once p-values don't have to be reported or taken into consideration anymore.
p = 0.05 is a pretty sensible value. It means that there is only a 5% chance that the results are random. That's a pretty safe assertion. If in some field p = 0.1 is more applicable, then well argue for it.
The initial stupid idea of course is when a journal decides to publish based only on "proven" statistical significance. Either a research paper has interesting material in it or it does not, whether it is statistically significant is an entirely different question and should be left for the reader to decide.
Come to think of it, of course, the whole peer review is one of the most stupid ideas of humanity. First of all, who are those peers that they think they can judgge someone's work? Usually the reviewers are neither current on some subject nor qualified to make a judgment call. Serious people know that and that's why they don't volunteer to be a peer reviewer. This essentially leaves all the morons that think they should have a say over others, people who feel better when they excert power on someone else.
Better take the social network approach: Have people vote on a paper. Much better results.
If capitalism is so great move to Russia. No they are not socialist anymore
In my International Relations graduate program there was a big push towards quantitative research and analysis; there were two mandatory classes on it. However, I always felt that it broke things down into too simplistic a view, and while it could tell things might be correlated, it never told you why. And with human systems like societies, states, conflict, politics, etc, there are so many inputs, so many factors that contribute to why people act the way they do, what decisions they make, that to boil it down to one or two that are "statistically significant" isn't missing the forest for a tree, it's missing the forest for a bush. Complex systems very often have complex inputs.
That's why I preferred a more qualitative approach: there was no arbitrary line of significance. It allowed you to explore more complicated or elaborate analyses. There was no worry about getting bogged down in what regression method you used or why, whether your math was wrong, or you excluded/included a variable that you shouldn't have. It gives you the chance to simply lay out your theory, your interpretation, and the evidence to back up that interpretation. And best of all, it allows you do it in such a way that it makes your research much more accessible to other people. I also prefer a more narrative style of writing anyway. Now, of course this for a humanities discipline. A more scientific discipline would require significantly more math.
The only thing necessary for evil to triumph is for it to be pitted against a slightly greater evil
This way research which appears interesting but which doesn't hit that magical p == 0.05 can be published and discussed
The significance value is essentially a measurement of how good a researcher is at their job. Unfortunately, a lot of academics feel that they shouldn't be bothered by silly things like "accountability", because they've chosen the noble ivory tower of research.
If your experiment can't hit that level of certainty, redesign your experiment. Go get more samples, run more simulations, and grow more cultures. Alternatively, go ahead and publish, but include the note that the job isn't actually finished. Use the partial result to justify asking for more funding so you can complete the work.
(These are all things I saw first- or secondhand during my time in academia)
I'd be fine getting rid of the p-value, but it would have to be replaced by something else that does an equal job of filtering out the half-assed crank "research" that makes more headlines than discoveries. The only replacement I can think of that wouldn't be vulnerable to similar "hack" methods would be to require that every experiment go through an exhaustive process inspection before, during, and after the run. That's an even more painful thing to deal with than making sure your experiment can produce significant results.
You do not have a moral or legal right to do absolutely anything you want.
Plus they are almost all from biology or medicine. Just because their fields don't seem to understand what statistically significant means does not mean that the rest of us do not. Their example when two results measure the same value but one is within one sigma of a null result and the other is not they claim that people interpret this as two incompatible results!? I do not know of any physicist who would look at those data and make that assertion.
Their paper reads more like a "I wish our colleagues understood simple statistics". Banning certain terms is not going to address the underlying problem they clearly have. The solution to ignorance is education, not censorship as they really ought to know, working in universities!
Even without a magical "significant/insignificant" threshold, researchers will still evaluate, judge, and compare levels of significance. The pressure will just shift to come up with results that are "MORE significant" rather than "LESS significant," and thus p-hacking will continue by those that were willing to cross that line in the first place.
The root cause is going to remain until peer reviewers force researchers to commit to how they're going to evaluate their measurements before they take those measurements. But the likely outcome would be either a lot less research would get published at all or published research would start to lose some of the imprimatur it now enjoys, including that of the peer reviewers. So that's unlikely to happen.
On average, humans have one breast and one testicle.
It's even worse when economic trends are reported in the popular press.
1 is prime by that definition, but it's mostly called a unit and defined as *not* prime to make factorising integers into primes unique (up to the order of the factors): Prime number - Primality of 1
Sure, in a perfect world we would all discuss the exact probabilities. The reality is we all (even professionals in an industry) have a limited attention span. Benchmarks are useful, even imperfect benchmarks. This is just another example of some purists thinking we should move to some idealized but impractical situation
I'm really curious about what people think about this comment and my attempt to defend p-values and statistical significance testing as a concept. I used to hate p-values like any respectable scientist, but in teaching intro college stats class (targeted to behavioral science), I've come to appreciate them, for one major reason.
1. We have to take uncertain science and make certain decisions about the conclusions. Science gets simplified to dichotomous decisions. You either approve the drug or not. You either eat eggs or don't eat eggs. The defendant is guilty or not guilty. In each of these cases, we take scientific and other evidence and have to make a decision: do we trust these data. Confidence intervals, odds ratios, etc, help give a picture but they don't give a clear guideline about what to accept.
2. It's really hard to understand (and teach) Bayesian and other approaches. I think that statistical significance is a decent proxy, as long as the limitations are well-understood. I am a big believer in teaching science research to people who have no desire to ever be "researchers", and in order to evaluate their studies, statistical significance is a good proxy. If you are doing an intro biology lab testing whether there are more bacteria on your hands after washing your hands versus hand sanitizer, a t-test with a p .05 criterion is a good approach. It won't get published in JAMA, but it's good for teaching research concepts.
3. Reviewers still want p-values. Each time I have submitted a manuscript without p-values, I get a nasty reviewer who requires p-values. Maybe I've had bad luck, but I'm guessing this is pretty common in the literature. Any time I try a statistical technique that goes beyond null hypothesis testing, there is at least one reviewer who doesn't understand the technique and gripes because there are no p-values or decision criteria. As long as this is required to publish, we need to do it.
So these aren't very good defenses, but it's why I'm still teaching p-values and null hypothesis testing. Maybe we will get rid of it, but like some other comments here, it leaves the question of what the alternative would be.
Mostly, they don't understand that the world isn't black and white.
People want answers. That's a given. And they used to turn to science for this. I say used to, because more and more people think that woo has better answers for their questions. The reason is less that science does not have answers, but that the answers science has require thinking and understanding. They are rarely YES or NO. There's a lot of ifs and buts attached, but people don't want that. They want easy answers.
And reality has rarely easy answers.
"Statistically significant" doesn't mean "resoundingly YES". But that was what was read into it, and of course that expected YES cannot be delivered.
Yes, reading statistics requires some effort by those trying to understand them. Unfortunately that's not what people want to do when they're looking for answers.
We used to have a Bill of Rights. Now, with the rights gone, all we have left is the bill.
If you understand what it means and how to apply it. If you blindly slap on the formula and use the resulting number to say, "Look, it's significant!", then, no, it isn't.
Trump is hiding in the capital. Once he says something stupid and untrue like he gets 4 more years (as if anyone would believe such a thing after all this time) the addled brains will pounce
It must be odd if it's prime.
Some drink at the fountain of knowledge. Others just gargle.
Correlation does not equal causation. How many times have "scientists" tried to convince us things that are obviusly false are true? Evolution, global waming, the "big bang", etc. All fake science used to push a liberal big goverment agenda. This article about Statistical Significance just further supports the fact that science is not useful.
The only real truth to be found is in the bible.
meh just set it to 0.051 and watch 90% of "science" publication burn
I think you will find all healthy humans have two breasts, the variable being size of development of mammary tissue from gender hormones.
As this indicates the statistic significance, or the average, is only as good as the data or research behind it.
The wait is over - YOU did!
"A prime number is divisible only by itself and 1
1 is prime (by this definition)"
When I was learning Maths (Mathematics is plural where I come from) I was taught that 1 is not prime, it is a special case.
Anyway for 1 the statement becomes:
1 is divisible by 1 and 1
But 1and1 is now IONOS
In a nutshell, what the statisticians are recommending is that we embrace uncertainty, quantify it, and discuss it, rather than set arbitrary measures for when studies are worth publishing. This way research which appears interesting but which doesn't hit that magical p == 0.05 can be published and discussed, and scientists won't feel pressured to p-hack.
People did that kind of publishing via folded papers in envelopes called "letters." Their purpose was to discus about interesting things and prepare the work towards a formal publication. Who knows, maybe such discussions could be possible even with today's technology?
Or you could have read his manifesto and not had any debate.
He thought Muslims were invaders that caused overpopulation. He was worried about overpopulation causing worse AGW and was taking care of the problem since no one else was. Called himself a socialist, eco-fascist.
But that bit of his manifesto didn't make the news and they are attempting to prevent people from being able to read it by pulling it down where they can.
Story about it.
And reality has rarely easy answers.
Which is why engineers answer most questions with "it depends".
Prime numbers have to be greater than 1 so 1 is not a prime. 2 is a prime because it's only divisible by 2 and 1. Any number who's sum of the digits is a multiple of 3 cannot be prime as it will be divisible by 3. Any number that end in 5 other than 5 cannot be a prime because it is divisible by 5.
Here is a table of all prime numbers up to 1,000:
2 3 5 7 11 13 17 19 23
29 31 37 41 43 47 53 59 61 67
71 73 79 83 89 97 101 103 107 109
113 127 131 137 139 149 151 157 163 167
173 179 181 191 193 197 199 211 223 227
229 233 239 241 251 257 263 269 271 277
281 283 293 307 311 313 317 331 337 347
349 353 359 367 373 379 383 389 397 401
409 419 421 431 433 439 443 449 457 461
463 467 479 487 491 499 503 509 521 523
541 547 557 563 569 571 577 587 593 599
601 607 613 617 619 631 641 643 647 653
659 661 673 677 683 691 701 709 719 727
733 739 743 751 757 761 769 773 787 797
809 811 821 823 827 829 839 853 857 859
863 877 881 883 887 907 911 919 929 937
941 947 953 967 971 977 983 991 997
There's nothing wrong with significance testing itself. What's wrong is when you have a strong motivation to have have significant result, which makes you have a significant result way more often than is to be expected, and when scientists do sloppy application of significance testing. As we all know, rarely are applications of statistics textbook in nature. There are little assumptions here and there that are made. These are little opportunities for bias. Worse, I'd bet that even most refereed papers are barely scrutinized well enough to confirm that the choice of statistical tests were apropos. For example, I've reviewed medical articles where everything was done right but sample size thresholds were not properly satisfied for the tests used and those are the EASY cases to spot.
https://en.wikipedia.org/wiki/...
Inheritance is the sincerest form of nepotism.
Slashdot.Org is full of athiestic animals who seem to ignore that the USA is turning into a africa style shit-hole full of undesireablals all based on liberal left wing stupidity. The truth of the bible helps people avoid the evils of multiculturalism and homosexuality and shuld be paid attention to instead of fake science and left wing terroristic politics.
If your experiment needs statistics, you ought to have done a better experiment.
Ever roll a d20? Ever get a Nat 20? There is a 1-in-20 chance that a p=0.05 is a "nat 20". If you re-run the study and have similar findings, there is a 1-in-400 chance.
p=0.05 indicates "try again and see".
The real problem is that replication isn't sexy. It isn't sexy to conferences, journals, funders, or even the replicators (I'm re-doing the work of others!). As such, we get to the 1-in-20 stage and not to the 1-in-400 stage.
the woosh is strong on this one.
The issue I find with nearly every single biological application of p-value testing is that either the wrong test is used, or, far more frequently, the necessary validations of the assumptions of the test have not been made. I assume that among those many articles from The American Statistician (a journal that I do not read) that point will have been made because although it is a subtle one, it isn't that subtle, and it is important.
The most commonly used statistical tests assume that unaccounted experimental variability will be Gaussian in nature. That assumption is patently false for the general case. Noise sources for some things are Gaussian -- thermal noise in an electrical signal for example -- but many, many biological sources are not.
When Nature is non-Gaussian, you have to be extra super careful with your tests of significance. And nearly every paper that I've read skips doing noise analysis to validate their tests. Even the lowly mean and standard deviation functions assume Gaussian variability for correct interpretation. The alternative is to have p-values that are so small that results are obvious by inspection --- and then you don't need statistics.
That's the sort of science I strive to perform.
Put my fist through my alarm clock with its ding-dong death inside my ear. - The Blackjacks.
If you browse around a typical statistics textbook, you will probably find a brief discussion about the difference between statistical significance and real world significance. It seems like a lot of people in sciences, specially in the soft sciences are chasing after the statistical significance because it's now some kind of a prerequisite to get published. However, their findings can amount to very little in the real world. Imagine for example that you find out there the commute distance is statistically significant between people who drink diet coke and tomato juice. Sounds like a great title for a click-bait report. But in reality, your estimates can be 7.34 miles vs 7.36 miles, a difference of 40 meters.
Yay.
So we can look forward to even MORE broken, badly researched, pointless garbage being published as academically or scientifically relevant.
Look at the finances of any journal pushing this crap. They're probably on borrowed time, in the financial sense.
Chas - The one, the only.
THANK GOD!!!
And this is why we don't make good politicians. Politics need easy answers. They needn't be correct or even solve anything, but they have to be easy to understand.
We used to have a Bill of Rights. Now, with the rights gone, all we have left is the bill.
Congratulations, you're the pea-brain who somehow dragged Trump into this first.
850 scientists and statisticians spouting this idea seems like a large number but compared to their total cohort around the globe I'm sorry but...
They are not statistically significant.
That and they are lazy.
By Michael Greenacre & Gurdeep Stephens
Barcelona, Catalonia & Victoria, Canada, March 2015
[Video link]
Statistics, logistics, cladistics seem to me
To have a common theme scientifically,
Economists, biologists, with PhD degrees,
They all need some proof of their theories.
A letter is the key, you'll see clearly,
Not B nor G nor V -- but it's the P !
There's no values like P-values
Like no values I know
Think of something that is not worth proving,
An hypothesis that everyone calls null,
If your P is too large to reject it
Then your experiment is rather dull.
There's no values like P-values,
Especially when they are low,
Don't be sad if your P's over point-O-five,
Just try again with samples twice the size,
Everything is possible, just trust in me:
Put your faith in the P.
The F test, the Z test, the chi-square and the T
And other cryptic terminology
Anova, regression, tests distribution-free,
They all need some sort of guarantee.
So if you find a tiny effect size
The P-value will be a good disguise.
There's no values like P-values,
The frequentist's hero,
When you get that data modeling feeling
But results you have are not a lot,
You will need some stats that are appealing
To show the journals your work is hot!
There's no values like P-values
Especially when they're low
Don't be sad if your P's over point-O-five,
Just try again with samples twice the size
Everything is possible, just trust in me:
Put your faith in the P!
We have too many researchers doing too many studies about the same topic and we incorrectly view each study as a separate event. Without quantification of the number of unpublished, published, and significant studies on a given topic, an individual study's relevance is unknown. If 200 separate researchers did 50 studies each (or 2000 did 5 studies) for a total of 10,000 studies, at .05 p, we could expect 500 false positives. When a study is published without knowing the universe of all studies on that topic, we do not know if any report of a significance level is really significant. Add that there is a bias to report and document positive over negative results. There is also data mining, where an existing database is used to search for any relationship among the historical variables at a p value and then report that relationship. With a large universe of studies and with data mining of historical data, an individual studies significance level is unknown and reproducible results is very low. Combining the data of published studies does not help, since their is a bias is what is reported and published.
We have too many researchers doing too many studies about the same topic and we incorrectly view each study as a separate event. Without quantification of the number of unpublished, published, and significant studies on a given topic, an individual study's relevance is unknown. If 200 separate researchers did 50 studies each (or 2000 did 5 studies) for a total of 10,000 studies, at .05 p, we could expect 500 false positives. When a study is published without knowing the universe of all studies on that topic, we do not know if any report of a significance level is really significant. Add that there is a bias to report and document positive over negative results. There is also data mining, where an existing database is used to search for any relationship among the historical variables at a p value and then report that relationship. With a large universe of studies and with data mining of historical data, an individual studies significance level is unknown and reproducible results is very low. Combining the data of published studies does not help, since their is a bias is what is reported and published.
you don't use statistical significance to say "true" or "False. Only mathematicians would do that
If you discard any choice because it is arbitrary, then that is itself an arbitrary choice and should, by your metric, be discarded and another found.
Why not 0.1? Why not 0.05. if you can't say why not 0.05, then why does anyone have to "explain" to you why not 0.1.
Yes, yes, yes, you're very smart for saying p-hacking live five thousand other fucking know-it-alls on slashdot. Here's the point, if p-hacking exists at 0.05, then what value will it NOT exist at?
What was that? No value of p value can be immune to it only made harder rr easier?
Well, then, what the fuck was the point of whining about p-hacking with a value of 0.05?
The problem is, like with the wording of the submitters, treating p-values as determining true of false. That's not what it is, and only statisticians and idiots would do otherwise, the latter because they don't know any better, and the former because that is what their realm of expertise uses it for, frequently.
Ask a stupid question ... GIGO. Social scientists give science a bad rep.
So please explain how he meant socialist a la Bernie Sanders and not a la what he's been taught Hitler was?
"Any number who's sum of the digits is a multiple of 3 cannot be prime as it will be divisible by 3."
Let's consider the binary number 10101. The sum of the digits is divisible by 3. Therefore, by your reasoning, the number 13 in binary is divisible by 3.
The problem with your logic is that our computations are done in binary, now, not in decimal. We've moved away from packed decimal a long time ago. :)
Unfortunately many scientists get this wrong and nearly everyone who inteprets results definitely make a profound mistake:
It is easy to think the choice is between insignificant and significant; if something is not insignificant, it must be significant.
But this is fundamentally wrong. There is a third choice: unknown. The real scale of significance is insignificant - unknown - significant.
Statistical tests can only be used to distinguish between the two leftmost choices: insignificant or unknown. They cannot prove your data contains the signal you were looking for.
A better way to see this kind of statistical analysis is:
* If the p value is over *treshold chosen, often 0.05*, your data is indistinguishable from random noise.
* If the p value is below your treshold, you data may or may not show a significant signal.
Correct me if I'm wrong, at CERN, they had to achieve S7 results, before they could conclude that they had found the Higgs-Boson particle. That was a lot of data that they worked with. This kind of accuracy would be hardly possible to achieve in other areas of science.
"Prime numbers have to be greater than 1 so 1 is not a prime."
According to your definition. Like most terms, there is no king to give the definitive definition. To me, prime is a cut of meat.
Also, remember that math is just a mental contruct that allows our human minds to interpret the universe around us.
Ninjas don't carry tic tacs
The even number 2
"All odd numbers are prime" does not imply "no even numbers are prime".
He's getting rather old, but he's a good mouse.
Actually 1 is neither prime nor composite by some deep mathematical definitions which go beyond the integers -- they go into the structure of algebraic rings which are generalizations of the integers. If you allow 1 (a unit) to be prime then you break some properties and theorems which everyone generally accepts in the algebra of the integers. The most well known such property is that of unique factorization -- any natural number is factored uniquely into prime factors. If you let 1 be prime then the prime factorization of a composite number can have any number of factors of 1 in it.
The deeper definition of a prime (from my old abstract algebra book) is, "In the Euclidean ring R a nonunit p is said to be a prime element of R if whenever p = ab, where a, b are in R, then one of a or b is a unit in R."
And there is a king which gives the definitive definition -- it is the accepted body of mathematical definitions by the world's mathematical community. There are sometimes differing definitions of a term, but those differences are usually well spelled out in any discussions. You can choose not to accept the definitions as the professionals in the field use them but then don't claim your definition is as good or useful as that of the pros.
Clearly, in human medicine, we can turn everything into a coin flip. gender, heredity, environment, culture, diet, all of those can be perfectly categorized and we can get 10 thousand subjects with this fairly rare disease to make a test that meets your required level of sensitivity.
Sure, in my branch of mathematics, I can give a very clear answer. Not so much in medicine or psychology.
p is well defined. Only idiots would want to abandon a useful tool to avoid its misuse by idiots. I think this is a corollary of one of the laws of Engineering: There's no such thing as foolproof; fools are far too clever. The fact that idiots will misuse and misinterpret it is a given. It WOULD be nice if we could keep the idiots out of the population of peer reviewers and journal editors, but I wouldn't hold my breath. The use of p-value should be discipline dependent, depending on the subject, the measurement, and the community. And most importantly, the details of the experiment.
9 isn't experimental error it's juts the one datapoint which contradicts your premis you couldn't plausibly deny having cherry picked out.
15 (not prime 3x5), 17, 19, 21 (not prime 3x7), 23, 25 (not prime 5x5), 27 (not prime 3x9), 29 ...
And in the otehr direction:
-1, -3 (not prime -1x3), -5 (not prime -1x5), -7 (not prime -1x7), -9 (-3x-3), 11 (-1x11)...
I don't care how significant your p value is, if your n is less than 40 case/control match your values are meaningless, other than proof of concept for further study.
Wake me up when you get 256/256 fully matched case/control with true randomization. Then we'll talk p values.
-- Tigger warning: This post may contain tiggers! --
Any major that has the word "studies" in its name is worthless.
My old stats prof taught us to read 'statistically significant' as 'not zero'.
"Any number who's sum of the digits is a multiple of 3 cannot be prime as it will be divisible by 3."
Fixed for pedants....
"For any integer n, base b > 1, divisor d of b-1, and other integer m congruent to n mod b-1, if d | m, then d | n."
Lies, damned lies, and statistics is a phrase describing the persuasive power of numbers, particularly the use of statistics to bolster weak arguments
https://en.m.wikipedia.org/wik...
Casteism
Go science! Scientists, our only gods of truth we can trust! Everyone else is a trump era liar, but scientists preserved the holy altar of truth.
There is a sucker born every minute and nobody cares. Enjoy everybody, you do nothing to prevent this so you deserve this.
I've always thought "statistical reliability" was a better name.