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ERWhere my Alex's at? From Tyler Vigen's Spurious Correlations #correlationdoesnotequalcausation
@ericryanericryan
#Spurious Correlation
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ERFrom Tyler Vigen's Spurious Correlations #correlationdoesnotequalcausation
@ericryanericryan
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ERWhere my Alex's at? From Tyler Vigen's Spurious Correlations #correlationdoesnotequalcausation
@ericryanericryan
1.4K
JAfor those who didn’t understand, please don’t take these graphs seriously 🙏
{spurious correlation, AI bias, machine learning fails, correlation vs causation, data science, AI risks, ML models, Tyler Vigen}
@jainnrranu
3.7K
DRCorrelation does not equal causation is one of the most common phrases in epidemiology. We can use extreme examples to point out that just because two things trend together does not mean they are causal. So next time you have a conversation on this topic, you can use examples like these! Check out Spurious Correlations from Tyler Vigen for more examples
#publichealth #science #scicomm #research #cdc
@dralexsundermann
622.4K
SHMistakes in our thinking are more common than we’d like to admit.
Sometimes, we mistakenly connect two things, assuming one caused the other.
It’s a pitfall we encounter all too often, especially when we’re interpreting statistics and research.
And let’s not forge the media’s role in this! Sensationalist headlines that link unrelated topics are like magnets for our attention – hello click bait!
Take, for instance, Time Magazine’s 2014 article. They reported on a study that found bullies had lower risk of chronic diseases, with the headline, “Bullying Is Good For Your Health.” 🤔
So, a word to the wise:
Always check your fact from fiction.
Have you come across this before?👇
(The ‘spurious correlation’ shared in the video was discovered by Harvard Law School student Tyler Vigen).
______
🔥P.S. My LAST FREE MASTERCLASS is in less than 10 hours! Last chance to join me live to level-up your career. Register using the link in my profile & stories!
(It’s free)
#correlation #spuriouscorrelation #criticalthinking #decisionmaking
@shadezahrai
18.7K
THThis image perfectly captures the hilarious and often confusing world of spurious correlations. In this classic example, we see a graph tracking two completely unrelated statistics over a decade: the number of people who drowned by falling into a pool and the number of films featuring Nicolas Cage.
At first glance, the lines follow a suspiciously similar path. If you only looked at the math, you might jokingly conclude that more Nic Cage movies lead to more pool accidents—or perhaps that pool safety inspires his acting career! Of course, we know that’s not true. This is the ultimate reminder that correlation does not equal causation. Just because two data points move together doesn't mean one causes the other. It’s a great lesson for anyone looking at data today: always look for the "why" before you believe the "what." Statistics can tell a story, but sometimes that story is just a funny coincidence.
@the_lastt_devil_
10.0K
SUA spurious correlation occurs when two variables appear to have a strong statistical relationship but are not actually linked by cause and effect; instead, their connection is usually driven by a third, unseen "confounding" variable or simple coincidence. A classic illustration of this is the relationship between ice cream sales and shark attacks: as ice cream consumption rises, the number of shark attacks also increases, creating a misleading graph that might suggest sharks are attracted to ice cream lovers. In reality, the hidden factor is warm summer weather, which simultaneously encourages more people to buy treats and more people to swim in the ocean where sharks live.
Similarly, the "Pirates and Global Warming" graph—famously popularized by the Church of the Flying Spaghetti Monster—shows a nearly perfect inverse correlation where the decline in the world's pirate population since the 1800s aligns with the steady rise in global temperatures. While the data points are technically "correlated," the lack of swashbucklers obviously isn't the cause of climate change. These examples serve as a humorous but vital warning in data science: correlation does not equal causation, and seeing two lines move together on a chart does not mean one is controlling the other.
#CorrelationVsCausation #InterestingFacts #DataVisualisation #DidYouKnow #LearningIsFun
@suspect.zero0
244
THFind out more about Spurious Correlations in a new YouTube video (link in bio)
@the___bias
2.6K
SK📊 Understanding Correlation in Statistics!
Ever wondered how ice cream consumption and accidents could be linked? 🍦🚗 It's all about correlation – the relationship between two variables!
🔹 1: Perfect positive correlation (both increase together)
🔹 -1: Perfect negative correlation (one increases, the other decreases)
🔹 0: No correlation
In the case of ice cream and accidents, it's a spurious correlation – they seem related but are influenced by a third factor: hot weather. 🌞
Remember, correlation doesn’t mean causation! Learn the difference and analyze smarter.
#SkillsYard #DataScience #CorrelationVsCausation #LearnStatistics #IceCreamFacts #SmartLearning
@skillsyard
76.6K
TEapay zeka modelinizin yüzde 99 doğruluk oranıyla çalışması, onun gerçekten öğrendiği anlamına gelmez. Deri kanseri teşhisi için eğittiğim derin öğrenme modelinde tam olarak bu problemi yaşadım. Modelin kanserli hücreleri mükemmel bir şekilde tespit ettiğini sanıyordum, ta ki açıklanabilir yapay zeka (Explainable AI) aracı olan Grad-CAM ile modelin karar verirken nereye baktığını görene kadar.
Model aslında kanserli dokuyu değil, veri setindeki doktorların tümörün boyutunu ölçmek için lekenin yanına koyduğu siyah cetvelleri ezberlemişti. Literatürde buna sahte korelasyon (spurious correlation) veya yapay doku sapması (artifact bias) deniyor. Algoritma, fotoğrafın içinde bir cetvel veya doktor işareti gördüğünde, kanserin yapısını analiz etmek yerine kestirme yoldan giderek doğrudan kanser teşhisi koymayı öğrenmiş. Makine öğrenmesi modelleri inanılmaz zekidir ama aynı zamanda en kısa yolu bulmaya çalışan tembel yapılardır.
Sadece Accuracy, Precision veya Recall gibi metriklerin yüksekliğine güvenip bir modeli canlıya alırsanız, gerçek dünya verisiyle karşılaştığında sistem tamamen çöker. Bu yüzden yapay zekanın kara kutusunun (black box) içine bakmak hayat kurtarır. Bilgisayarlı görü (computer vision) projelerinde sadece modeli büyütmek yetmez, veri sızıntılarını (data leakage) engellemek ve YOLO gibi algoritmalarla sadece hedef bölgeyi izole etmek gerçek mühendisliktir.
Sizin model eğitirken veya veri seti hazırlarken başınıza gelen en ilginç veri sızıntısı veya aşırı öğrenme (overfitting) problemi neydi? Yorumlarda mimariyi ve çözümleri tartışalım.
makine öğrenmesi, derin öğrenme, yapay zeka, bilgisayarlı görü, veri bilimi, yazılım mühendisliği, python programlama, açıklanabilir yapay zeka, veri sızıntısı, algoritma optimizasyonu, bilgisayar mühendisliği, yapay zeka mühendisi, model eğitimi.
!!!Önemli: Görseller, eğitim ve proje geliştirme amacıyla ISIC (International Skin Imaging Collaboration) arşivinden alınmıştır.
@technull.1
105
THHere is the final of our three live segments on @fox56news talking about spurious correlation with some interesting trends that would suggest a causal relationship. Does ice cream increase shark attack risk? Does your consumption of margarine in Maine determine your divorce risk? We talk about it.
Check out the ACEP Frontline podcast later this week for a breakdown of the articles used by the administration to make their position on the Tylenol/Autism correlation. Podcast available on most popular platforms.
@thedocisin_stantonmd
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