Originally posted by HallsofIvy
First, I have absolutely no idea what you mean by "Statistical Probabilities". Are there other kinds of probabilities?
Second, I have no idea where you got the idea that, in any kind of probability, "the outcome is intepreted as a fundamental 'Law like' process. "
For one thing, there are usually many "outcomes" not "the outcome". Also, in mathematical probablility, we are typically "given" the underlying probability distribution but I would not call that 'Law like'.
If that is what you are talking about, then, no, it is not "observer dependent". Of course, when you are applying a mathematical model to a statistical problem, you might approximate the given distribution by one based on observation.
Finally, it's impossible to say how "accurate" a "guess" is.
In statistics, accuracy is typically measured by the difference between the estimated value and the true value. This can be quantified using various metrics such as mean absolute error, mean squared error, or root mean squared error.
It depends on the context and the level of accuracy desired. A guess can be considered accurate if it falls within an acceptable margin of error and is supported by evidence or data. However, a guess cannot be considered accurate if it is arbitrary or based on biased information.
Yes, a guess can be used to make predictions in statistics, but the accuracy of the prediction will depend on the quality of the guess and the data used to support it. It is important to note that making predictions based solely on a guess is not considered a reliable or accurate method in statistics.
The sample size can greatly impact the accuracy of a guess in statistics. In general, the larger the sample size, the more accurate the guess will be. This is because a larger sample size provides a more representative and diverse range of data, reducing the likelihood of making an inaccurate guess.
Yes, statistical methods can be used to improve the accuracy of a guess. These methods involve analyzing and manipulating the data to make more informed and precise estimates. Some common statistical methods used for this purpose include regression analysis, confidence intervals, and hypothesis testing.