Validate Algorithm Statistically: Questions & Answers

[rickdatech]

I understand a bit about math, but it's been over 30 years since I've been to college. I have a question about where to find information mostly. My prolbem is that I have an algorithm that guesses the results of a process. The process has as best I can figure about 10 degrees of freedom.

1) how many times would I need to run the algorithim against the process to statistically validate the algorithm?

2) where would I find an Internet source that discusses this type of statistical validation? none of the google terms I come up with work for me.

I understand I can prove it by showing the math of how the algorithm was constructed, but that only works if you know the "why" behind the algorithm.

 

[EnumaElish]

1. No less than 30.
2. Look up statistical regression analysis.

 

[tacman]

To validate an algorithm statistically, you would need to run it multiple times against the same process and compare the results. The number of times you need to run the algorithm would depend on the level of confidence you want to have in the results. Generally, the more times you run the algorithm, the more accurate and reliable the results will be. This can also vary depending on the complexity of the process and the algorithm itself.

As for finding information on statistical validation, there are many resources available online. Some good places to start would be academic journals, research papers, or websites of reputable organizations such as the American Statistical Association or the International Statistical Institute. You can also consult with a statistician or data analyst who can provide guidance on the best methods for validating your algorithm.

In addition to proving the math behind the algorithm, it is also important to thoroughly understand the underlying principles and assumptions behind it. This will help in determining the appropriate statistical tests and methods to use for validation. It may also be helpful to consult with experts in the field of the process you are trying to predict, as they can provide valuable insights and feedback on the algorithm.

Overall, validating an algorithm statistically requires a combination of mathematical understanding, practical application, and collaboration with experts in the field. It may take some time and effort, but it is crucial in ensuring the accuracy and reliability of your algorithm's predictions.