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I blame Christmas and lack of sleep - I misread the actual article. Article is completely wrong. " ... found a p-value of 0.18. What does this mean? It sugges

by RyanZAG 14y ago

I blame Christmas and lack of sleep - I misread the actual article. Article is completely wrong. " ... found a p-value of 0.18. What does this mean? It suggests that there is no evidence of clustering beyond what you would expect from a random process ... " Above is incorrect. What the author is actually testing is the following statement: The mass shooting data points come from a Poisson distribution with an average value of 2 However, the test he is using can only prove this statement to be false: it CANNOT prove that the statement is true. So the only thing the author proved is that the statement may or may not be true. (For more, read up on a null hypotheses, the scientific term for this.) All the author has proven is that there is an 18% chance that the data does not come from a Poisson distribution. ~1 in 5 chance of being wrong is not something you assume to be right. Author needs to learn statistics.

Tloewald 14y ago
All any statistical twst can do is give you a probability. Nothing can be "proven". In any event, the possibilities are not "random" and "not random", e.g. We could be seeing a poisson distribution with a very small copycat effect. What is pretty clear is that the US has an obscenely high rate of gun violence, which is what we ahould be debating. Several thousand children die each year, and nearly one and a half Sandy Hook's occur each day.
dbecker 14y ago
All the author has proven is that there is an 18% chance that the data does not come from a Poisson distribution. This isn't right either. You've committed the inverse fallacy [1]. The correct way to read that p-value is: "If the data came from a Poisson(2) process, it would be 18% likely to have deviations at least as different from poisson(2) as the observed data." The test gives no insight into the probability that the true distribution is Poisson or Poisson(2). [1] http://en.wikipedia.org/wiki/Confusion_of_the_inverse http://en.wikipedia.org/wiki/Confusion_of_the_inverse
- tel 14y ago
  Worth saying "18% of independent repetitions would be so severe" as well! :)

by RyanZAG 14y ago

Tloewald 14y ago

All any statistical twst can do is give you a probability. Nothing can be "proven". In any event, the possibilities are not "random" and "not random", e.g. We could be seeing a poisson distribution with a very small copycat effect. What is pretty clear is that the US has an obscenely high rate of gun violence, which is what we ahould be debating. Several thousand children die each year, and nearly one and a half Sandy Hook's occur each day.

dbecker 14y ago

All the author has proven is that there is an 18% chance that the data does not come from a Poisson distribution. This isn't right either. You've committed the inverse fallacy [1]. The correct way to read that p-value is: "If the data came from a Poisson(2) process, it would be 18% likely to have deviations at least as different from poisson(2) as the observed data." The test gives no insight into the probability that the true distribution is Poisson or Poisson(2). [1] http://en.wikipedia.org/wiki/Confusion_of_the_inverse http://en.wikipedia.org/wiki/Confusion_of_the_inverse

tel 14y ago
Worth saying "18% of independent repetitions would be so severe" as well! :)