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Heads tends to be heavier (not sure if this is a general rule). Discussion of US penny: http://www.smithsonianmag.com/science-nature/gamblers-take-note-the-od

Heads tends to be heavier (not sure if this is a general rule). Discussion of US penny: http://www.smithsonianmag.com/science-nature/gamblers-take-note-the-odds-in-a-coin-flip-arent-quite-5050-145465423/ http://www.smithsonianmag.com/science-nature/gamblers-take-n... But with a coin flip, the initial side up is a bigger bias.

KirinDave 9y ago
… Its precisely statistics that can help us identify these trends in coin flips and if there actually is observable effects to these weights.
- kbutler 9y ago
  Are you seriously asserting that climate models are as direct an application of basic statistics as a coin flip? Statistics work really well when we understand the system, the initial state, inputs, and can accurately measure the results. None of those apply to climate models, especially regional models. Statistics make it easy to mislead yourself that the precision of your numbers implies correctness - like the joke, How do you know economists have a sense of humor? They use decimal points.
  - KirinDave 9y ago
    > Are you seriously asserting that climate models are as direct an application of basic statistics as a coin flip? No. Never did. > Statistics work really well when we understand the system, the initial state, inputs, and can accurately measure the results. ... This is actually the opposite of the truth. When we understand the system fully and can fully enumerate it's interal states and every relevant input, that's when there is no point in statistical modeling. I really think you fundamentally misunderstood your statatistics education.
    - kbutler 9y ago
      If you don't know the system and the range of inputs you don't know what to model. If you can't measure the outputs, you can't validate your statistical model. The predictions for the current solar cycle are a good example - the predictions were drastically off, the actual values outside the predicted error ranges from the statistical models. It was expected to be a more active solar cycle, based on our understanding of the system, its inputs, and the state from the previous cycle. Because we didn't (don't) understand the system, our models were wrong. If your model is not right, leaving out inputs, having the wrong range of variables, etc., it doesn't matter how good your math and statistics are, your model works...until it doesn't. Then you get to do science and generate a new hypothesis, model that, and try again! What you can't do is say in 2011, "Models show California will be drier because of global warming" then in 2017, "Models show California will be wetter because of global warming" and claim the models (both?!) have predictive power. "This will not do. You never will be able to make both of them good for any thing. Take your choice, but you must be satisfied with only one. There is but such a quantity of merit between them; just enough to make one good sort of [model]" --Elizabeth Bennett (And no, there's not nearly enough "drier" area in the new model to be compatible with the older predictions.)

KirinDave 9y ago

… Its precisely statistics that can help us identify these trends in coin flips and if there actually is observable effects to these weights.

kbutler 9y ago
Are you seriously asserting that climate models are as direct an application of basic statistics as a coin flip? Statistics work really well when we understand the system, the initial state, inputs, and can accurately measure the results. None of those apply to climate models, especially regional models. Statistics make it easy to mislead yourself that the precision of your numbers implies correctness - like the joke, How do you know economists have a sense of humor? They use decimal points.
- KirinDave 9y ago
  > Are you seriously asserting that climate models are as direct an application of basic statistics as a coin flip? No. Never did. > Statistics work really well when we understand the system, the initial state, inputs, and can accurately measure the results. ... This is actually the opposite of the truth. When we understand the system fully and can fully enumerate it's interal states and every relevant input, that's when there is no point in statistical modeling. I really think you fundamentally misunderstood your statatistics education.
  - kbutler 9y ago
    If you don't know the system and the range of inputs you don't know what to model. If you can't measure the outputs, you can't validate your statistical model. The predictions for the current solar cycle are a good example - the predictions were drastically off, the actual values outside the predicted error ranges from the statistical models. It was expected to be a more active solar cycle, based on our understanding of the system, its inputs, and the state from the previous cycle. Because we didn't (don't) understand the system, our models were wrong. If your model is not right, leaving out inputs, having the wrong range of variables, etc., it doesn't matter how good your math and statistics are, your model works...until it doesn't. Then you get to do science and generate a new hypothesis, model that, and try again! What you can't do is say in 2011, "Models show California will be drier because of global warming" then in 2017, "Models show California will be wetter because of global warming" and claim the models (both?!) have predictive power. "This will not do. You never will be able to make both of them good for any thing. Take your choice, but you must be satisfied with only one. There is but such a quantity of merit between them; just enough to make one good sort of [model]" --Elizabeth Bennett (And no, there's not nearly enough "drier" area in the new model to be compatible with the older predictions.)