The method that can "prove" almost anything - James A. Smith
HTML-код
- Опубликовано: 21 авг 2024
- Explore the data analysis method known as p-hacking, where data is misrepresented as statistically significant.
--
In 2011, a group of researchers conducted a study designed to find an impossible result. Their study involved real people, truthfully reported data, and commonplace statistical analyses. So how did they do it? The answer lies in a statistical method scientists often use to try to figure out whether their results mean something, or if they’re random noise. James A. Smith explores p-hacking.
Lesson by James A. Smith, directed by Anton Bogaty.
Support Our Non-Profit Mission
----------------------------------------------
Support us on Patreon: bit.ly/TEDEdPat...
Check out our merch: bit.ly/TEDEDShop
----------------------------------------------
Connect With Us
----------------------------------------------
Sign up for our newsletter: bit.ly/TEDEdNew...
Follow us on Facebook: bit.ly/TEDEdFac...
Find us on Twitter: bit.ly/TEDEdTwi...
Peep us on Instagram: bit.ly/TEDEdIns...
----------------------------------------------
Keep Learning
----------------------------------------------
View full lesson: ed.ted.com/les...
Dig deeper with additional resources: ed.ted.com/les...
Animator's website: / anton_bogaty
Music: www.campstudio.co
----------------------------------------------
Thank you so much to our patrons for your support! Without you this video would not be possible! Sylvain, Can Aydogmus, JasonD, Terran Gimpel, Gareth Thomas, Talia Sari, Phan Quang Lam, Katie McDowell, Allen, Sarat Chandra Vegunta, Mahina Bachiller, Bruce Vieira Lopes, Charmaine Hanson, Paul Aldred-Bann, Thawsitt, Jezabel, Abdullah Abdulaziz, Adriano Fontes, Xiao Yu, Melissa Suarez, SpartacusDMR, Brian A. Dunn, Francisco Amaya, Daisuke Goto, Matt Switzler, Leonardo Monrroy, Sumedh Ghaisas, Maryam, Bethany Connor, Jeremy Shimanek, Mark Byers, Avinash Amarnath, Xuebicoco, Rare Media, Rayo, Po Foon Kwong, NinjaBoffin, Jesse Jurman, Josue Perez Miranda, Scott Markley, Elija Peterson, Ovidiu Mrd, Xavier dupont, paul g mohney, Aravind Battaje, Nathan Giusti, Helen Lee, Anthony Benedict, Karthik Balsubramanian and John Hong.
I'm gonna be replaying this lots before I understand it
The p-value is the likelihood that the result was due to random chance.
A p-value of 5% is considered statistically significant, but there's still a 5% chance it was only due to random chance and not a real effect.
If you do 40 studies on whether mobile phone masts cause cancer you expect to find one statistically significant study saying they do, one statistically significant study saying they reduce cancer risk and 38 studies saying they don't make a statistically significant difference. If you only publish the one study out of 40 saying they increase cancer risk you've now hacked the p-value by not publishing the 39 studies that didn't show the result you wanted. Taken together those 40 studies would have a p-value closer to 50%, but you only looked at the one with the p-value you wanted. If you also publish the study saying masts statistically significantly reduce cancer risk, you now have two conflicting outliers as your only results and people will be tempted to come up with some other factor to explain why one group had positive and another had negative health effects.
Same 😅
@@soylentgreenb thanks for this brief summary 👀🙌🏻
@@soylentgreenb thank you
@@soylentgreenb I'm gonna be reading this lots before I understand it
There is a .014 chance that I just understood that video
Lol. Good one! XD
Well, in simple terms it's cherrypicking.
When you're doing an experiment, there are chances that the results don't represent the reality by pure chance. For example, if you wanted to know what is the male to female ratio in the population, you could go somewhere and count the number of male and female. In theory, that ratio should be close to 1 : 1. However, supposing you chose the perfect place, there is still a chance that the population is, say, majoritarily women for no specific reason other than luck.
Let's say you wanted to prove that there were twice as many men as women. You could go to a ton of places until one of them gives you the ratio you expect, and only report that result. That's the idea behind p-hacking.
In reality, to be efficient, you could do the experiment by gathering lots of different datapoints for a specific experiment. If one of these datapoints gives a significant unexpected result, only report that one.
@Richard Long so ... did you? or did you not?
@@EnteiFire4 I did kinda understand the entire p-hacking thing but I want to ask how to avoid or reduce it. Like if we collect data from different points do we just report the unusual data or report all the data.
The P value animation is adorable
Also - engagement.
hey, great picture amigo =)
You mean it's *P* erfect? Haha
He's just vibing honestly
Only animation?! )))
The method that can "prove" almost everything: _H A C K I N G_
haha I like your name, is it a reference to something? =)
I hate these terms.
@@ScumfuckMcDoucheface Thanks! Yep, it's a line from an old comedy called "Bringing up Baby". It's a nice watch :D
not to be associated with hacking, the method that can IMprove almost anyone
Phacking
"Science doing science on itself..."
Yo dawg, we heard you liked SCIENCE!
Excited Jesse Pinkman emerges.
Let's call it "scientology", oh wait.
"Metascience"?
Good ol' meme, haven't heard that in a long time.
the first thing I saw was "torture the data" **shocked Pikachu face**
That is certainly a pickle.
**T-TORTURE** 😳😳😳
Another way to say that might be, "Force the data to fit a desired result."
Gasp...but that would mean science could be "rigged" or "faked" to produce a desired outcome.
Gee...the possibilities would rapidly become endless for powerful people with obscene amounts of money...and an agenda. Hmm... 🤔
Make sure it spills everything
@@Ghost12314 picklerick??!!!
2:00 Not quite: The P value is the probability you'd have gotten a result _at least as extreme_ as you did, _just due to chance,_ assuming the null hypothesis is true.
That still makes no sense
This was honestly not a very good explanation. Even as someone who somewhat (!) gets the concept of p-hacking I was pretty confused throughout most of the video and couldn't follow the logic.
For example I've watched the part at 1:25 and after several times to try to understand what they're trying to say and I think it's that if she sorts the cups correctly and the p-value is low then there is a good reason to believe that she was actually able to taste the difference. But something so simple is worded so poorly that I had to infer that from my own knowledge and reasoning and I feel like if this was the first video someone watched about this topic it wouldn't be very helpful.
Or maybe I'm just exceptionally slow, that's possible as well...
The p-value explanation is lacking, but the p-hacking explanation is ok IMO.
I second this! First TED-Ed video that failed to explain to me what they were trying to explain to me.
To be fair to them they are trying to explain a concept that many scientists misunderstand in a 5 min video.
Yes exactly. The basic idea is we want to see how likely something happened purely by chance versus because of some actual cause. Say your friend claimed they have a supernatural ability to predict a coin flip. After you skeptically tested them, you find that they got around 50 out of 100 correct. That's not really impressive enough for you to reject your belief that they have no special ability: since we expect anybody to get around 50% just by pure chance. If however you found that they got 99 out of 100 correct, you have more evidence to assume they might have some special ability since this is way better than pure chance. It would be a highly unlikely event to happen under the assumption that they have no ability.
I didn't get anything tbh
I learned Statistics for 2 semesters and this is still confusing.
Yep
Honestly I think unlike previous ted videos, this one is throwing some random words without detailed explanation (like null hypothesis, appeared here and there, and explained partially each times)
@@ericw2391 honestly,this was a total waste of time...99% people didn't learn a single thing and that is the whole point of watching ted ed video to learn something new....so they should be careful about topics which we can understand..
@@gohilravirajsinh5212 lol pre-requisite to this video: AP stat or higher education
phack statistics
I think I can provide a fairly simple explanation to convey what this video tried to do.
Imagine you are trying to see if eating snow cones causes mind-reading. Your partner thinks of a number from 1-5, and you eat a different colored snowcone each experiment. Just based on probabilities you will guess it 1 out of 5 times on average, so, you eat 5 different colored snow cones before you guess the number right.
This is the data:
Orange snowcone - No
Red snowcone - No
Green snowcone - No
Blue snowcone - No
Yellow snowcone - Yes
Therefore, you publish the last data result showing that eating yellow snowcones leads to mind-reading. Of course, this is a simplification but it conveys the general idea that by increasing the data collected and ignoring certain results, you can prove something just by sheer probability. Had we repeated the yellow snow cone experiment 5 times, we would have seen it was luck but by neglecting the other data we were able to make a false conclusion.
So then why would researchers use it? (I’m disagreeing with you, just asking)
@@sammarrasheed3000 To reduce computation costs and time I believe?
Basically they're trying to prove what they believe through statistical information which is an hypothesis. right?
Don't eat yellow snow, mate...
@@sammarrasheed3000 Because they're eirher getting paid by a company or corporation to do so and come out with specific results that'll favor them, or because they have a personal bias that'll benefit themselves and/or whatever their cause might be.
Torture the data till it confesses.
-what I learned
This kind of method which "proves" almost anything is widely used by corporates especially in consumer products to justify their "product's" credibility from research.
I agree, but to avoid this issue, researchers who "pre-register" a plan for the experiment cannot manipulate the data and analysis. As a result, " This Method" would defend the corporates' results with validity and reliability concerning their "products."
Do you think this might apply to vaccination studies?
"four out of five experts surveyed recommend our product."
@@wliaputs it applies to antivaxxers.
@@wliaputs Definitely not.
They preregister their studies, and real-world performance matches the predicted performance.
I remember learning this in statistics class! Nice to see a video on this topic. Thank you Ted-Ed!
There is a slightly more subtle way in which this happens. Even when an experiment is planned in advance there is likely to be a vested interest in proving the hypothesis. So studies that do not do this are not finally published (studies that prove hypotheses are more cited for example) so when one looks for the number of studies that support a hypothesis (rather than the quality) it is easy to conclude that a certain hypothesis is true just because there are more documented cases.
Video recap: P-value cannot tell us if the reverse of null hypothesis is true when the null hypothesis is rejected. Instead, using p-value can only point in the right direction that one possibility is ruled out because of how (i.e., the terms) the null hypothesis is stated. Meaning, if the p-value rejected the null hypothesis, it just puts a “not” (i.e., alternative hypothesis) in front of the null hypothesis thus the reverse is still technically not true based on the phrasing of the null hypothesis. Thus, this result would only narrow down towards the reverse possibly being true. So, the pair of hypotheses statements (null and alternative) would need to be rephrased to prove if the reverse is true. Even when true, the tests need to be repeated for repeated “reverse-proven” results. Moreover, the irony here increased testing means increasing the chance of false negatives. So, there is no perfect p-value but still use the p-value to narrow down results.
I’ve been a ted Ed fan for years and this artist is definitely one of my faves. Thanks for all you guys do!
Hi, Ted Ed
Michael here
Selin here
Harry here
Mix here!
Chris here
I feel like everyone is high watching this except for me: the stories are so clever, clear to understand and clean-cut animation, but videos like this blow my mind. I’ll never stop watching!
You don't have to be 'high' to be smart - it just takes a lot of time and effort. 🙂
That was phacking educational.
The fact that P keeps tumbling into the experiment and ruining it is hilarious.
"Sometimes small true true different from the big true true"- jerry smith.
That's big true
Correction ~ Cloud Atlas Jerry😉
I struggled to understand this until now🤣
I don't understand statistics like mean, mode and median.
Is that normal?
well if you understand concept of "normal" then you already understand mean/average
Let's say the data we are lookin at is how many candies a bunch of kids have. That data is: 2, 2, 3, 4, 10
The mean is the average of that data. (2 + 2 + 3 + 4 +10) / 5 = 4.2 candies
The mode is the more frequent data value. The mode here is 2, because two kids have 2 candies.
The median is the value which splits the data 50-50. Two kids have less than 3 candies, two kids have more than 3 candies, one has exactly 3 candies. The median is 3.
I hope that clarifies things for you
@@SKyrim190 Legend 🙏
@@SKyrim190 incredible good example
it's taught in the 6th or 7th grade, so I guess it depends on your age...
No one:
Me reading the title: *PHACKING*
(F)acking
I was looking for thus comment
Now I am remembering the days when I used to understand every video of Ted-Ed at once and it used to be the best explanation on the entire internet. :(
its not you, the explanation was terribly bad
Yes, it's true..m
I learned about this in stats! I'm so surprised that I can kinda understand this because the terms "null hypothesis" and "p-value" and the bell chart are familiar to me! :D
Putting milk in before the tea
Englishman: death it is then. Slow and painful or drawn out and agonising?
I did a statistic course, this is one of the first things they tell you to do otherwise you can just BS the p-value to fit or change other variables to prove your desired hypothesis.
I just offered my family milk tea with breakfast. I told them the P value was only 0.015, but nobody wanted to drink it.
hahaha yeah I pee in my family's tea too!! hahaha wait, what?
Fascinating stuff! This is analogous to optimization bias in machine learning, or even the idea of a standard of proof (e.g. "beyond a reasonable doubt") in a criminal trial.
Test: “You have a .0000000003% chance of winning by guessing.”
Scientists: *“This simply won’t do.”*
🤣
The common p value of 0.05 would mean that you can reject your null hypothesis with a confidence of 95%. So you're giving a 5% chance to be wrong about your idea. That's about 1 BILLION times more than your little funny point here states.
Everyone's gangsta until the P-value starts waving to the reseracher....
I knew all those years of LSD would catch up with me =) haha yeeehawww!!
Really, really liked this video. I could tell you only went into a little detail on what is basically a piece of ubiquitious mathematical chicanery, but since I love that sort of stuff, thanks!
I am not first, I am not last
but whenever I get your notification I click very fast
Your 18th just to share Somen fax
Some not somen
Omg from so many days, I was searching for a simple explanation for P value and here it is! TED-Ed is a saviour! Thank you for making this video and helping me to understand the concept in an easier manner.
I learned new stuff from this too, don't know what's up with the people who said that they find it confusing
and hard to understand. I mean, they are talking simple/basic explainations for even the ones who haven't heard of this before could take grasp of it.
This video is going straight to my statistics study playlist on RUclips!
I feel so smart! I knew exactly what they were talking about throughout the entire video! Someone hand me an award! 😂
I'm not so smart.....I only understood the first half then slowly got more confuuuusseedd 🤯🤯😭🤪
I'm awarding you a Gold Medal 🥇💐
@@julieugolini4195 from what I have seen most people are equally smart, but what sets people apart is their interest in a particular subject or field, which is what determines how knowledgeable they are. Now regarding the video if you had to study statistics as a main subject or as a part of your undergraduate course in college then you would easily understand what the narrator was saying throughout the entire video.
@@gohansesshomaru8400 seems I'm awarding you a medal also 🥇💐 you sound really smart,, I admire people that are either born smart or work hard at education to get smart
I unfortunately did not get to University (your college) due to some head Trauma but I have had to think about your reply and you have just cleared something in my thinking that my Neurosurgeon has been trying g to explain to me for years
Thankyou so much
Yes you are correct, I'm smart about life because I'm 63yrs old so have probably more wisdom than what I call "smart"
I have struggled with some memory loss last couple of years and beating myself up when I get confused.
So once again, thank you your reply has done a good thing
Cheers Julie 🇦🇺😍
@@julieugolini4195 please don't be hard on yourself life is what it is, never meant to be perfect (as a 25 year old, my intention is not to lecture someone more than twice my age on life but that my words bring atleast some satisfaction to you). So glad to hear that my first comment meant something to you, I'm just a guy who wants to know more about this world as I slowly grow older. Lots of love from a small coral island in the Arabian sea(Minicoy if anyone cares), sincerely hope that your life goes better than you ever expected.
XOXO, Shamsheer
🏅🏆
That's called Family Wise error. When you keep doing T-Tests, because each time you do one, the chance of getting a type 1 error. That's where ANOVA'S come in. Analysis of Variance. So you can test Three or more Variables to find where the difference lies, as opposed to where the difference DOESN'T lie. Simply put, T-Tests are for 2 variables, ANOVA's are for Three or more 😁
Actually, it’s just where Bonferroni corrections come in, which account for the family-wise rate. If you have two variables, but one has multiple levels, you could still be doing t-tests to look at differences between those levels.
I didn't understood a thing!
FyreStryder and use Benjimini Hochberg for when you have thousands of tests.
Actually, Romello-Eisingen is a better one, which allows for the Heinrich rate. And finds the Schneider value of the Hoffman-Bianchi levels.
People tell me Im significant, must be p-hackers
The thumbnail: "P" Hacking
Me: PHACKING
This needs to be widespreadly known...
I think it would be interesting to discuss how while there is an easy solution to make experiments more reliable in design, that solution doesn’t work in reality because researchers are ENCOURAGED to P-hack based on the incentives their job provides. When negative results see much less publication, and a researchers job depends in part on getting papers published, they sometimes have to game the system to get a false positive result just so that they can keep their livelihood.
Maybe someone can help me. If the null hypothesis is that she can’t tell the difference. And if the chance of her randomly getting it right is .014. And that is what she gets, then wouldn’t that mean the null hypothesis holds? In that, she can’t tell the difference which is the null hypothesis.
This video seems to be saying that just because the p-value is less than the constant .05 then she can tell the difference. But I feel like this isn’t how math works. Like all you have to do is increase the sample size and all of a sudden you have a p value smaller than the arbitrary constant .05.
Simplified to:
Suppose she can’t tell the difference, hence the null hypothesis.
Null hypothesis Suppose she can’t tell the difference
=> p-value=.014
=>p-value < .05
=> She can tell the difference
Null hypothesis fails
It is very wordy and bad explained tbh.
In VERY simple words, it's like this:
Null hypotheses usually state the results you intuitively expect and they don't go alone. They come with an alternative hypothesis, which can be the opposite result. So in our case:
Null: She CAN'T tell the difference
Alternative: She CAN tell the difference
When you state the "more likely result", you pretty much give a 95% chance for it to happen, and the rest 5% goes to the alternative (hence the 0.05 limit). So, when she gets a p-value of 0.014 what that means is basically "she got it right even though I expected her not to". Therefore, there is an indication that this is result is probably not by chance and so you reject the null hypothesis.
Hint: Be careful of the wording, you never "accept" a hypothesis (either null or alternative). You reject one and you're left with the other. It doesn't sound really concrete, I know, but that's how it basically works.
Btw, by increasing the sample size, you don't necessarily get smaller p-values, rather with each increase you get a p-value closer to the true one. Whether this is above or below 0.05
The smaller a p-value, the less likely a concrete correlation between measured values, or the validity of a positive assertion is. Larger p-values mean that the null hypothesis is more likely to be correct. The 0.05 p-value measure is just a statistical standard, a broad convention followed across researches to assert the significance of a finding.
This explanation is plain wrong or they are trying to prove something by explaining it wrongly just like p-hacking.
The video says 1:11 "if she cant distinguish the teas she'll still get the right answer 1 in 70 times by chance." *No* she wont get the right answer because, the chance of that hapening is 1 in 70 or 1.4% which is actually less than impossible(impossible is capped at 5% thus 0.05). But if she can distinguish the teas and she got it in one try then, like I said, it would be so impossible in fact it is less than impossible 0.014 < 0.05, that it would be a miracle it happened and we would be forced to accept the hypothesis the she has the miraclous power of predicting tea mixture and thus
reject the null "she cant predict".
Note that when I say impossible, I mean less than or equal to 0.05, that is the standard and that is how a hypothesis is accepted.
p < .05 = null rejected, hypothesis accepted
p > .05 = null cannot be rejected, but does not mean null is true
Alright, time to flip coins until I get 5 heads in a row to prove that coin flips are not random.
This video is extremely convoluted
Ted-Ed can you do a History On Trial of Oliver Cromwell.
Oooooo, great idea.
Ah yes, now I understand the Dream scandal.
After all these years, I finally understand why my high school and college papers are all "reject the hypothesis".
First TED-Ed video that failed to explain to me what they were trying to explain to me.
For me : Not the 1st { commentary #fail } 😕🤦▶️
Ted Ed: The method that can “ prove ” almost anything
Me: Joe Rogan saying it. Bam, no need for this lesson.
If you take seriously that clown, I just 💀
@@sof1675 Aah Yes, Joe Rogan the clown, who also happens to be successful at almost everything he's tried.
Very good subject. Relevant question at a time when science is the product of tasks with predefined goals and not of thoughts and ideas.
The animation style is amazing.
I love how this is in the same style of animation as the Rasputin music video.
Thank you so much for throwing light on this with such entertaining visuals..loved every aspect of it...Ted-ed is one of the consistently best sources of learning for me for a long time & looking forward to even more..thank u folks 💓
Having said that I feel that a more detailed elaboration might help more especially for folks like me who are not exactly the brightest bulbs..
Ah yes, I remember studying this in college.
I failed the paper twice.
I am almost 100% certain I did your state lecture at Ted Vancouver.
This is clearly an issue that is more prevalent in certain fields, whereas in others that's just very basic knowledge. That's why clinical trials publish their statistical analysis plan ahead of time, and why you have to limit primary and secondary endpoints. As for early discovery/biology work, that's why you do multiple testing correction.
To anyone who is confused, I've tried to explain the content of the video a bit:
The point of the video was that a lot of researchers keep trying different methods until they find something big, then they only report the significant results. This method has a huge problem: if you test something a lot, you get a lot of false positives. We call this method P-hacking (we'll get to why later).
In statistics, the P value should be the chance that the whole research is wrong. As an example, a P value of 0.05 means there is a 5% chance that the research is wrong (and 95% chance it's correct). However, if the researcher uses the previously mentioned method "P-Hacking", then there is a much higher chance of a false positive. This means that the P value (a.k.a. probability of messing up) is much higher than reported, and nobody can prove otherwise.
This is a big problem, because a lot of researches have done this without even realizing that they were making a mistake.
If anyone is still confused, feel free to discuss it in the replies.
Impressive video, TED Ed. Seriously, one of my college professors use your videos as additional study materials, in addition to some TED talks related to our topic. Thank you very much.
Edit: grammar correction
My questions:
1. 3:42 Why father’s age can be used for “control for variation in baseline age across participants”?
2. 3:52 What’s the significance of “they also paused their experiment after every ten participants”? Why did they do that? What impact could it make?
3. 3:55 Why would they “continue when the p value was above .05 but stopped when it dipped below .05”? I thought we want the p value to be below .05. So shouldn’t they have done the opposite?
There's not really anything wrong with using the presence of low p-values to assert the degree of relatedness of studied variables; however, p-hacking has become something like 'lying through omission' in several fields, especially psychology. Selective blindness, confusing correlation with causation, and sometimes, even the deliberate obfuscation of facts has led to the Replication Crisis in psychology.
isn't this case can be categorized as selective bias by those scientists?
I don’t think that’s how the p-value works, the fact that 1/70 is lower than 0.05 is insignificant
it isnt, different studies have different level of significance, p-value is a way to show readers or other researchers to let them decide if the value is significant enough to reject the null hypothesis.
The fact that its lower than 0.05 means its significant, if its higher then it doesnt mean its insignificant, just that that we cannot reject the opposite hypothesis.
Pre-registering and all would be great. What would be even greater is a link to the study.
4:42: my brain has exploded with the amount of times they say “science” here
The P value character is for sure a narcissist 😭
Ted-Ed is such an amazing channel! They offer all these amazing and interesting educational videos that they work so hard on for free! I find this so much more interesting than school! Keep up the good work!
sometime yes :D
1:53 2:42 - She's suspicious, she is. I hope I don't have nightmares because of her!
As for the P-value, I was thinking 'Why not make some strong rules to abide by?' And yep, at 4:24 the pre-registration/rules had been considered to ensure the experiments didn't continue until the desired result was achieved.
normal people: hacking with cute P animation :D
Me: PHACKING
Speedruner:Does 6 hour long speedrun only to cheat in the last few minutes.
Speedrun Moderator: Listen here you little __
FREE MEME
You smell good when you sleep
The probability of me getting this data is 0 percent
There's a high chance of me not understanding this video, and I can't calculate the chance of me understanding this video with p-value
Normal people looking at the thumbnail: P (value) hacking!
My brain: P H A C K I N G
So is this an equivalent to “cherry-picking “ or cooking the books?
Loving the animation, but I'm a little sad that you didn't include the history behind this. For example, the tea animation was just set with some random character that looks rather male (but hey if it's a girl no problem), but you're referring them as "she" and "her", this lady was actually some mistress that claimed she didn't like her tea poured first, and rather the milk. Because it just tastes better, so they made a test to see if she could get it correct. She got every answer correct! By this to be random chance, I believe it was around a 1 in 70,000 chance. This chance they calculated was the first "P" value calculated to test the probability.
And another instance of P hacking, was when a guy went to the market for a dead, gutted salmon. They put it in the MRI, they did a series of tests and they showed that the P value was proving that the salmon was alive and showing brain activity, showing that the P value can be manipulated to prove impossible results.
This is why science is broken. Rather than using experimentation to disprove a theory, data and analysis is cherry picked to suit a preconceived agenda. Thank you TED-ED for having the courage to post this video, bringing light to the problem with modern science.
Videos like this only prove more and more that statistics are always more complicated than you think.
This is another reason why we have peer review.
Unfortunately a lot of the people who do the peer review are unpaid and/or don't get any credit for it. The larger journals can do pretty good peer reviews for the papers they publish but they cost a ton of money to get published in; whereas the smaller or online journals can't do as much peer reviewing so their papers aren't taken as seriously. It is something that needs to be addressed in my opinion.
Statistics mean simultaneously everything... and nothing.
Yes it means nothing in the future, but for now it determines our daily lives.
no.
I remember learning the p value and the null and alternate hypothesis in high school but now that I truly understand it's use
You should have mentioned power. In the analysis one can make two types of errors - I and II. p-values are kind of a check for type I errors and power is a check for type II errors. But the power of a test usually is considerably harder to evaluate, so people just don't bother. In research reproducibility is one of the main conditions. A result must be reproducible. No pre-planning would prevent people from reporting only what they want to report. In the example from the video they could fake data of the 3d experiment using other two as a template.
P-hacking is very useful when your science has an agenda. ;)
When I read the tiltle I thought they would simply say multiply both side of an equation by zero and "prove" RHS=LHS lol 😅😅
In a sense, this is p-hacking: showing only one algorithm that produces a significant result (multiply by 0) and ignore everything else (like multiplying by any other value)
🤣😂🤣😂
@@enacrt ya
Why don't you mention anywhere the title of the actual research and the names of the researchers?
Very good explanation of P. Well done.
He should get a PhD in- " irrational rationalisation".
First!
Beat me to it by 10 secs
Beat me to it by 30 seconds.
@@4Nulla you really are!
Congrats for being first!
I'm so amazed and grateful that there is a TED-Ed video about the p-value. I think I understand it a liiittle better now ;)
'P' is more friendly than Miss minutes:)
Albert Einstein: I'm the most intelligent person on the world.
Stephen Hawking: No i am.
The person who watch all the entire videos from TedEd:
Hold my neurons.
Flip a coin 5 times, note down the results. Congratulations, your result had only a 1/32 < 0.05 chance of occuring! Very significant.
Torture the data long enough until it confesses! Thought provoking video! Music is really very interesting, somehow it made understanding the P concept easier.
This is one of my favorite animations
👍👍👍
I just want this method to prove my math question
PROVE THAT
LHS = RHS
The _'P'_ value comes as a result of drinking so much tea.
Just send my Nobel in the post, thanks.
this is extremely common in my uni.
No one:
Not a single soul:
Anti-vaxxers: I aM gONnA USe tHiS tO pRoVE THat vAcCineS aRe GOveRNmEnt spIeS
Govern-ment = control your mind
The most ununderstandable Ted-Ed video I've watched
Thumbnail: P HACKING
Me: Phacking? Weird.
Publishing a research plan beforehand will not prevent the researchers from doing more experiments than planned and only releasing the favourable ones. However, publishing a research plan has a great advantage that even researches with negative results will get published, because it's another unrelated, but a very significant problem: currently publishing a negative research result is deemed to not be a significant contribution to science.