3 ms·

It's always nagged me that statistical problems are scoped so small. Surely in saying there's 6 outcomes on a dice you'd obfuscated the billions of interactions

It's always nagged me that statistical problems are scoped so small. Surely in saying there's 6 outcomes on a dice you'd obfuscated the billions of interactions between atoms and input possibilities in doing so. Thrower A and thrower B will undoubtedly throw slightly different which might actually constraint the outcomes and skew the 1 in 6 percentages? It's similar to me to condensing 30 characters to 5 via an algorithm. You can go one direction but not the other and if your model was centered around the resulting 5 it doesn't really reflect what's actually happening which may skew the probabilities quite a bit. e.g. if the algorithm was "if first letter is not q, then first letter in output is q". If you were saying each has an equal percentage of occurring it'd be flat out wrong. * I am not a statistician and have no idea what I'm talking about

ianamartin 9y ago
For these kinds of exercises about fair dice and fair coins, it's a lot like the kind of jiggery-pokery that happens in Physics classes and stuff. "Under ideal circumstances . . .", "In a perfect vacuum . . .", etc. Outside of games, for which this was explicitly created, these kinds of things are learning tools to understand distributions and dependent vs. independent events, and it also makes a separate point about assumptions. In reality, if we were wanting to predict the outcome from a real dice-throwing event, we would either sample the results from actual people throwing dice, or we would simulate the results based on parameter inputs for exactly the types of things you are talking about. Of course, no one really cares that much about dice, other than people who play board games with dice. :) So substitute any other stochastic method for generating an outcome that does matter, and the statistical approach will generally be the same: either sample or simulate. Obviously there are exceptions, but that's really the basic idea.

ianamartin 9y ago

For these kinds of exercises about fair dice and fair coins, it's a lot like the kind of jiggery-pokery that happens in Physics classes and stuff. "Under ideal circumstances . . .", "In a perfect vacuum . . .", etc. Outside of games, for which this was explicitly created, these kinds of things are learning tools to understand distributions and dependent vs. independent events, and it also makes a separate point about assumptions. In reality, if we were wanting to predict the outcome from a real dice-throwing event, we would either sample the results from actual people throwing dice, or we would simulate the results based on parameter inputs for exactly the types of things you are talking about. Of course, no one really cares that much about dice, other than people who play board games with dice. :) So substitute any other stochastic method for generating an outcome that does matter, and the statistical approach will generally be the same: either sample or simulate. Obviously there are exceptions, but that's really the basic idea.