#scientific-method — Public Fediverse posts

Astrophysics PhDs DEBUNK Flat Earth with the Scientific Method

https://www.youtube.com/watch?v=GQ74BRrkFK0

#Astrophysics #Science #ScientificMethod #FlatEarth

Не верящий в динозавров Игорь Ашманов и отрицающий эпилепсию Василий Генералов приняты в Академию ВРАЛ

22 марта в Петербурге вручали премию за наибольший вклад в распространение лженауки в минувшем году. Звания «Почётного Академика ВРАЛ – 2025» удостоился известный IT-специалист и предприниматель, к.т.н. Игорь Ашманов.

Жюри, состоящее из ученых, присудило Игорю премию за публичные высказывания, в которых бизнесмен отрицает биологическую эволюцию, заявляет, что не верит в самопроизвольное развитие языков и «в динозавров». Победитель, по правилам премии, награждается призом — статуэткой «Грустный рептилоид». Кроме того, в финал премии вышли математик, член-корреспондент РАН Алексей Савватеев, известный выступлениями в поддержку креационизма и гомеопатии, и дизайнер Артемий Лебедев, отрицающий глобальное потепление и заявляющий, что смертность от СПИДа «это вообще ни о чем». В народном голосовании большинство голосов досталось Алексею Савватееву.

Член жюри, Академик РАН Евгений Александров выразил озабоченность тем, что лженаука проникает внутрь РАН — «престиж Академии наук важен, потому что люди доверяют ей».

«Почётным Академиком АПЧХИ» — главным распространителем лженауки в области медицины жюри из медиков признало д.м.н., невролога Василия Генералова, отрицающего эпилепсию как диагноз, продвигающего лечение аутизма у детей методами с недоказанной эффективностью и пугающего аудиторию последствиями прививок. Победитель удостоился оздоровительного приза — «Золотой кофейной клизмы». Членом-корреспондентом АПЧХИ выбрали д.м.н., хирурга Владислава Шафалинова, связывающего рост числа онкологических заболеваний с вакцинацией и электромагнитным излучением, предлагающего лечить рак «ощелачивающей терапией» и другими сомнительными методами. Врач, д.м.н. Сергей Бубновский, призывающий лечить широкий спектр заболеваний исключительно гимнастикой, не получил ни одного голоса жюри и поэтому в Академию АПЧХИ принят не был. «Приз зрительских симпатий» достался Владиславу Шафалинову.

Руководитель оргкомитета премии ВРАЛ научный журналист Александр Соколов выразил удивление по поводу того, что в финале медицинской премии впервые оказалось 3 доктора наук и задался вопросом: «Неужели ученая степень в области медицины значит так мало?» Ведущая премии, врач к.м.н. Ольга Жоголева выразила надежду, что сейчас, когда научно обоснованная медицина набирает обороты, есть шанс, что ситуация изменится к лучшему.

В рамках мероприятия состоялись выступления биолога, к.б.н. Ильи Удалова на тему «Так в чём Дарвин не прав?» и аллерголога, к.м.н. Ольги Жоголевой «Правда о БАДах».

Организатор премии — научно-просветительский портал Антропогенез.ру и проект «Ученые Против Мифов».

Среди членов жюри: академик РАН, д.ф.-м.н. Евгений Александров, д.г.н. Алексей Екайкин, д.х.н.Игорь Дмитриев, д.г.н. Ольга Соломина, д.ф.-м.н. Эмиль Ахмедов, д.г.н. Елена Сухачева, д.б.н. Тамара Кузнецова, д.и.н. Кирилл Назаренко, д.м.н. Юрий Сиволап, д.м.н. Сергей Поликарпов, к.м.н. Юлия Зинченко, к.м.н. Анна Дроганова, к.м.н. Игнат Рудченко и др.

Полный состав жюри

ВРАЛ — Вруническая Академия Лженаук, АПЧХИ — Академия Превентивной ЧакроХирургии.

Премия «Почётный Академик ВРАЛ» присуждается с 2016 года. По словам организаторов, цель премии — в шутливой форме заявить о проблеме лженауки и привлечь внимание общественности к важности борьбы с заблуждениями.

Пресс-релиз
Официальный сайт премии
Подробно о финалистах
Полная видеозапись мероприятия
Главные вопросы о премии ВРАЛ

Наши партнеры: SciTopus MedIQ#ВРАЛ #Антропогенез #УченыеПротивМифов #Лженаука #Псевдонаука #НаучноеМышление #Скептицизм #КритическоеМышление #НаучПросвет #ScienceCommunication #Darwin #Evolution #EvidenceBasedMedicine #AntiPseudoscience #FactChecking #ScienceEducation #RAN #Ashmanov #Generalov #ЛженаучныеМифы #ПремияВРАЛ #ScienceVsMyths #ScientificMethod #PublicScience #Medicine #Biology #Vaccination #EvidenceBased #Debunking

@nazokiyoubinbou @Natasha_Jay
Ah, quite right, the tendency of physics questions to edge toward unrealism...
https://xkcd.com/669/
#xkcd #physics #science #scientificMethod #experiments

This was posted about a month ago, but is still good. It is a set of 9 principals based on statements from Carl Sagan dealing with figuring out what is B.S.

https://bigthink.com/starts-with-a-bang/carl-sagan-detecting-baloney/

#CarlSagan #ScientificMethod #Hypotheses #HypothesesPruning #CombatFakeNews

6th grader's science fair project answers age-old question: 'Do cat buttholes touch everything?'

https://web.brid.gy/r/https://www.upworthy.com/cat-butthole-science-experiment-goes-viral-ex1

More than 400 years ago, Johannes Kepler challenged deeply held assumptions about how the universe should work 🌌
By following the data, he transformed astronomy and showed that planets move in ellipses, not perfect circles.

A short video worth watching 👇
https://youtu.be/3tOhNRjIHVc?si=tUXNxEhWRrau6O9X

#Astronomy #HistoryOfScience #ScientificMethod

#ScienceHistory #Curiosity

A quotation from Claude Bernard

But it happens further quite naturally that men who believe too firmly in their theories, do not believe enough in the theories of others. So the dominant idea of these despisers of their fellows is to find others’ theories faulty and to try to contradict them. […] They make experiments only to destroy a theory, instead of to seek the truth. At the same time, they make poor observations, because they choose among the results of their experiments only what suits their object, neglecting whatever is unrelated to it, and carefully setting aside everything which might tend toward they idea they wish to combat.
 
[Mais il arrive encore tout naturellement que ceux qui croient trop à leurs théories ne croient pas assez à celles des autres. Alors l’idée dominante de ces contempteurs d’autrui est de trouver les théories des autres en défaut et de chercher à les contredire. […] Ils ne font des expériences que pour détruire une théorie, au lieu de les faire pour chercher la vérité. Ils font également de mauvaises observations, parce qu’ils ne prennent dans les résultats de leurs expériences que ce qui convient à leur but, en négligeant ce qui ne s’y rapporte pas, et en écartant bien soigneusement tout ce qui pourrait aller dans le sens de l’idée qu’ils veulent combattre.]

Claude Bernard (1813-1878) French physiologist, scientist
An Introduction to the Study of Experimental Medicine [Introduction à l’Étude de la Médecine Expérimentale], ch. 3 (1865) [tr. Greene (1957)]

More info about (and translations of) this quote: wist.info/bernard-claude/81484…

#quote #quotes #quotation #qotd #claudebernard #bias #confirmationbias #disproof #experimentation #hypothesis #proof #science #scientificmethod #theory

There is no certainty in science. Today's 'fact' is tomorrow's flat earth.

#CriticalThinking
#science
#ScientificMethod
#logic
#facts

What is the scientific process, from investigation to publication? Mark Louie Ramos, an expert in health policy and administration, explains for @TheConversationUS.

https://flip.it/VjbC2y

#Science #ScientificMethod #Research

A quotation from Isaac Asimov

Science doesn’t purvey absolute truth. Science is a mechanism. It’s a way of trying to improve your knowledge of nature. It’s a system for testing your thoughts against the universe and seeing whether they match. And this works, not just for the ordinary aspects of science, but for all of life. I should think people would want to know that what they know is truly what the universe is like, or at least as close as they can get to it.

Isaac Asimov (1920-1992) Russian-American author, polymath, biochemist
Interview (1988) by Bill Moyers, A World of Ideas, PBS TV (1988-10-22)

More info about this quote: wist.info/asimov-isaac/34218/

#quote #quotes #quotation #qotd #isaacasimov #knowledge #life #process #science #scientificmethod #technique #test #truth #philosophy

I deny nothing but doubt everything. - Lord Byron

#CriticalThinking
#science
#ScientificMethod

RE: https://mastodon.social/@safest_integer/115303726020952780

One of the best papers I've just read on AI not being AGI

Three clear conclusions from the paper

1. Humans must remain at the creative steering wheel
2. AI can mimic humans but cannot explain like humans or create explanations
3. Current forms of AI cannot match human intelligence

#ai #research #philosophy #science #scientificmethod

Cutting Down Ockham's Razor via OpenMind Magazine [Shared]

We hear all the time that the simplest explanations are usually the right ones. This truth-testing idea—known as Ockham’s razor, after the English medieval philosopher William of Ockham—has been embraced by no less authorities than Isaac Newton and Albert Einstein. Today scientists invoke Ockham’s razor on topics ranging from Covid’s origins to cosmic dark matter, while folks debating a subject on social media regularly invoke it as their final arbiter. After all, why complicate something more than you need to? Isn’t it better to shave ideas down to their essential truths?

https://welchwrite.com/blog/2025/10/01/cutting-down-ockhams-razor-via-openmind-magazine-shared/

#ockham #ockhamsrazor #simple #complex #ideas #explanations #science #scientific #scientificmethod #shared

A few words from Sabine Hossenfelder
https://www.youtube.com/watch?v=ZO5u3V6LJuM
#SabineHossenfelder #physics #science #ScientificMethod

There’s a strong urge to believe what you wish instead of what you can prove. Computer rumors are a great example. Many rumors have no basis other than being a feature someone wants. They call it "wish casting".

We want the world to be black and white. Some given statement is either true or false. But it’s not. Gödel #Godel describes at least three states: true, false, and unprovable (e.g., the statement "This statement is false". Can’t be true or false; it’s unprovable. Maybe there’s a better name.)

But it’s worse than that.

In science, a theory isn’t true … it’s just the best explanation we have so far. The whole endeavor of science is to keep finding better explanations. To make good decisions you don’t need the absolute best explanation, just one good enough to guide you to beneficial choices. (I said "prove" before, but to be more accurate I should be talking not about what you can prove, but about what you can’t disprove.)

#Bayes (really #Laplace) says a given notion isn’t true, it’s actually true-with-some-probability. Each new thing you observe impacts that #Probability. This is the actual math behind the #ScientificMethod. And it’s the truth of the world. Your beliefs must adapt to your observations, constantly, forever.

If you have unshakable faith in some set of "facts", you’re probably doing it wrong. Even when you’re right, you could be righter.

Of course, if you don’t adjust your beliefs with new input, if you don’t test, if you have "facts" instead of "very probable theories". If you believe things because of how strongly the person who convinced you believed instead of what they could actually show you. If you believe simply because that’s what your parents taught you. Then, well, you **might** be right (even a stopped clock is right twice a day). But at best you’re not going to make good decisions for yourself, and at worst you’re going to try to tell others what to do based on an inaccurate understanding.

It’s messy; and that’s just how it is.

No hypothesis is scientific unless new evidence could potentially prove it false.

Application: Making the claim that a particular idea is science, one should ask, how could that idea be proven false? For example, the claim that Intelligent Design is a scientific theory. Counterargument: How could Intelligent Design be falsified? Answer: it is unfalsifiable, therefore #IntelligentDesign is a belief.

#CriticalThinking
#science
#ScientificMethod
#logic

Elizabeth Finkel, Vice-Chancellor's Fellow at La Trobe University, Australia, looks at the Trump administration's constant, baseless skepticism, philosopher Karl Popper's definition of science as the search for truth, and how the scientific method — gathering and testing of facts — is the basis for democratic institutions. "The history of authoritarian regimes tells us when ideologues take over science, it does not end well," writes Finkel for @theconversationau. "It was the Nazi takeover of German universities that saw the likes of Einstein seek refuge in the US — and turned America into a scientific superpower."

https://flip.it/MpQzWe

#Science #ScientificMethod #TrumpAdministration #Kennedy #Trump #Philosophy

Science is built on inquiry grounded in controlled observation and logic. This ensures we can accurately link changes to their causes. #ScientificMethod

Saw a institute announcing their latest #data #science #astrophysicist #professor #hire, saying how passionate he is about his #hobby, piloting planes. Really? In this day and age, we are proud of that? Should I say I love beating animals as a hobby? If you still love #flying today you are either bad at #physics or dislike #data, or you don't believe in the #scientificmethod. #WTF #climatechange #astrodon #AcademicChatter

My Road to Bayesian Stats

By 2015, I had heard of Bayesian Stats but didn’t bother to go deeper into it. After all, significance stars, and p-values worked fine. I started to explore Bayesian Statistics when considering small sample sizes in biological experiments. How much can you say when you are comparing means of 6 or even 60 observations? This is the nature work at the edge of knowledge. Not knowing what to expect is normal. Multiple possible routes to a seen a result is normal. Not knowing how to pick the route to the observed result is also normal. Yet, our statistics fails to capture this reality and the associated uncertainties. There must be a way I thought. 

I started by searching for ways to overcome small sample sizes. There are minimum sample sizes recommended for t-tests. Thirty is an often quoted number with qualifiers. Bayesian stats does not have a minimum sample size. This had me intrigued. Surely, this can’t be a thing. But it is. Bayesian stats creates a mathematical model using your observations and then samples from that model to make comparisons. If you have any exposure to AI, you can think of this a bit like training an AI model. Of course the more data you have the better the model can be. But even with a little data we can make progress. 

How do you say, there is something happening and it’s interesting, but we are only x% sure. Frequentist stats have no way through. All I knew was to apply the t-test and if there are “***” in the plot, I’m golden. That isn’t accurate though. Low p-values indicate the strength of evidence against the null hypothesis. Let’s take a minute to unpack that. The null hypothesis is that nothing is happening. If you have a control set and do a treatment on the other set, the null hypothesis says that there is no difference. So, a low p-value says that it is unlikely that the null hypothesis is true. But that does not imply that the alternative hypothesis is true. What’s worse is that there is no way for us to say that the control and experiment have no difference. We can’t accept the null hypothesis using p-values either. 

Guess what? Bayes stats can do all those things. It can measure differences, accept and reject both  null and alternative hypotheses, even communicate how uncertain we are (more on this later). All without making assumptions about our data.

It’s often overlooked, but frequentist analysis also requires the data to have certain properties like normality and equal variance. Biological processes have complex behavior and, unless observed, assuming normality and equal variance is perilous. The danger only goes up with small sample sizes. Again, Bayes requires you to make no assumptions about your data. Whatever shape the distribution is, so called outliers and all, it all goes into the model. Small sample sets do produce weaker fits, but this is kept transparent. 

Transparency is one of the key strengths of Bayesian stats. It requires you to work a little bit harder on two fronts though. First you have to think about your data generating process (DGP). This means how do the data points you observe came to be. As we said, the process is often unknown. We have at best some guesses of how this could happen. Thankfully, we have a nice way to represent this. DAGs, directed acyclic graphs, are a fancy name for a simple diagram showing what affects what. Most of the time we are trying to discover the DAG, ie the pathway of a biological outcome. Even if you don’t do Bayesian stats, using DAGs to lay out your thoughts is a great. In Bayesian stats the DAGs can be used to test if your model fits the data we observe. If the DAG captures the data generating process the fit is good, and not if it doesn’t. 

The other hard bit is doing analysis and communicating the results. Bayesian stats forces you to be verbose about your assumptions in your model. This part is almost magicked away in t-tests. Frequentist stats also makes assumptions about the model that your data is assumed to follow. It all happens so quickly that there isn’t even a second to think about it. You put in your data, click t-test and woosh! You see stars. In Bayesian stats stating the assumptions you make in your model (using DAGs and hypothesis about DGPs) communicates to the world what and why you think this phenomenon occurs. 

Discovering causality is the whole reason for doing science. Knowing the causality allows us to intervene in the forms of treatments and drugs. But if my tools don’t allow me to be transparent and worse if they block people from correcting me, why bother?

Richard McElreath says it best:

There is no method for making causal models other than science. There is no method to science other than honest anarchy.

#AI #BayesianStatistics #BiologicalDataAnalysis #Business #CausalInference #DAGs #DataGeneratingProcess #dataScience #ExperimentalDesign #FrequentistVsBayesian #Leadership #machineLearning #philosophy #science #ScientificMethod #SmallSampleSize #StatisticalModeling #StatisticalPhilosophy #statistics #TransparentScience #UncertaintyQuantification

Why don't Americans trust experts? 😂 Because they'd rather take advice from someone who claims to have seen a ghost than from a PhD. 🧙‍♂️✨ Apparently, the scientific method is out, and crystal balls are in! 🔮🔍
https://bigthink.com/big-think-books/paranormal-investigators-public-trust/ #trustissues #ghoststories #scientificmethod #skepticism #humor #HackerNews #ngated

@emma that was an excellent watch, thank you! Will definitely check out more of her videos.
#science #scientificMethod #faith #education #dogmatism etc.

"Allergy fear leads boy to create sand that repels fire ants"
"Capable of decomposing viruses, dirt and other organic materials into water and carbon dioxide, photocatalysts have a wide range of applications"
"The boy mixed [ #photocatalyst ] slurry with coral sand"
"His aim was to disorient ants and prevent them from working in groups by breaking down [their] trail pheromones"
"[He] received the #Tokyo governor's prize at an invention contest"
https://www.asahi.com/ajw/articles/15682645
#ScientificMethod #Japan

Here is something less funny on this #april1 that sounds like a joke, but isn't:
I want to use a certain big data set and asked the first author of the paper presenting it where some parameters used come from, after the reference they claimed they used didn't show these values. The answer below is what I got and I found it quite puzzling.
The params used are reasonable values, but how much can I trust that data now?

#science #reliability #scientificmethod