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Chasing Coincidences - Issue 4: The Unlikely - Nautilus - 0 views

nautil.us/...chasing-coincidences ToK math psychology probability coincidences
shared by markfrankel18 on 23 Nov 13 - No Cached
  • The simple question might be “why do such unlikely coincidences occur in our lives?” But the real question is how to define the unlikely. You know that a situation is uncommon just from experience. But even the concept of “uncommon” assumes that like events in the category are common. How do we identify the other events to which we can compare this coincidence? If you can identify other events as likely, then you can calculate the mathematical probability of this particular event as exceptional.
  • We are exposed to possible events all the time: some of them probable, but many of them highly improbable. Each rare event—by itself—is unlikely. But by the mere act of living, we constantly draw cards out of decks. Because something must happen when a card is drawn, so to speak, the highly improbable does appear from time to time.
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How a mathematician dissects a coincidence - YouTube - 0 views

www.youtube.com/watch math luck coincidence probability
shared by Lawrence Hrubes on 17 Feb 17 - No Cached
  • Can you unknot a twist of fate with logic? Vox's Phil Edwards asked mathematician Joseph Mazur about his book, Fluke, and one of its most incredible stories.
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Correlation is not causation | OUPblog - 0 views

blog.oup.com/...correlation-is-not-causation ToK correlation science reason
shared by markfrankel18 on 20 Nov 13 - No Cached
  • A famous slogan in statistics is that correlation does not imply causation. We know that there is a statistical correlation between eating ice cream and drowning incidents, for instance, but ice cream consumption does not cause drowning. Where any two factors –  A and B – are correlated, there are four possibilities: 1. A is a cause of B, 2. B is a cause of A, 3. the correlation is pure coincidence and 4., as in the ice cream case, A and B are connected by a common cause. Increased ice cream consumption and drowning rates both have a common cause in warm summer weather.
  • We know that smoking causes cancer. But we also know that many people who smoke don’t get cancer. Causal claims are not falsified by counterexamples, not even by a whole bunch of them. Contraceptive pills have been shown to cause thrombosis, but only in 1 of 1000 women. Following Popper, we could say that for every case where the cause is followed by the effect there are 999 counterexamples. Instead of falsifying the hypothesis that the pill causes thrombosis, however, we list thrombosis as a known side-effect. Causation is still very much assumed even though it occurs only in rare cases.
  • One could understand a cause, for instance, as a tendency towards its effect. Smoking has a tendency towards cancer, but it doesn’t guarantee it.. Contraception pills have a tendency towards thrombosis but a relatively small one. However, being hit by a train strongly tends towards death. We see that tendencies come in degrees, as do causes, some strongly tending towards their effect and some only weakly.
  • ...1 more annotation...
  • Correlation does not imply causation. At best it might be taken as indicative or symptomatic of it. And perfect correlation, if this is understood along the lines of Hume’s constant conjunction, does not indicate causation at all but probably something quite different.
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VSauce-Spooky Coincidences - 2 views

www.youtube.com/watch science math psychology perception
shared by Cisco Walker on 20 Sep 14 - No Cached
  • Cisco Walker on 20 Sep 14
     
    Mathematical and psychological explanations for the once in a million and pareidolia.
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Does This Ad Make Me Fat? - NYTimes.com - 1 views

www.nytimes.com/...does-this-ad-make-me-fat.html science knowledge reason
shared by markfrankel18 on 10 Mar 13 - No Cached
  • A team of researchers walked every street in 228 census tracts around Los Angeles and New Orleans and recorded every outdoor ad they saw. Another group surveyed 2,881 residents of the same census tracts by telephone, paying them to report their height, weight and other information. After analyzing this hard-won data, the authors conclude: “For every 10 percent increase in food advertisements, the odds of being obese increased by 5 percent.” That is, areas with more outdoor food ads have a higher proportion of obese people than ones with fewer ads.
  • The problem is that their policy recommendations rest on a crucial but unjustified assumption: that any link between obesity and advertising occurs because more advertising causes higher rates of obesity. But the study at hand showed only an association: people living in areas with more food ads were more likely to be obese than people living in areas with fewer food ads. To be fair, the researchers correctly note that additional steps would be needed to prove that food ads cause obesity. But until those steps are taken, talk of restricting ads is premature. In fact, it is easy to imagine how the causation could run the opposite way (something the article did not mention): If food vendors believe obese people are more likely than non-obese people to buy their products, they will place more ads in areas where obese people already live. Suppose we counted ads for fitness-oriented products like bicycles and bottled water, and found more of those ads in places with less obesity. Would it then be wise anti-obesity policy to subsidize such ads? Or would the smarter conclusion be that the fitness companies suspect that the obese are less likely than the fit to buy their products?
  • When we seek to base policy on evidence, we must remember that not all “evidence” is created equal. Taken at face value, the study on ads and obesity provides some indication that the two are linked, but no evidence that food ads cause obesity. The fact that the causal conclusion may coincide with a moral belief — that it is wrong to tempt people who overeat by showing them ads for food — does not make it valid.
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