Statistics interview questions for machine learning on Type I vs Type II error, random number generation, and Bayesian vs frequentist inference.
Type I error is a false positive, while Type II error is a false negative. Briefly stated, Type I error means claiming something has happened when it hasn’t, while Type II error means that you claim nothing is happening when in fact something is. A clever way to think about this is to think of Type I error as telling a man he is pregnant, while Type II error means you tell a pregnant woman she isn’t carrying a baby.
It generates a pseudo random number based on the seed and there are some famous algorithm, please see below link for further information on this. [src]
Frequentist statistics is a framework that focuses on estimating population parameters using sample statistics, and providing point estimates and confidence intervals.
Bayesian statistics, on the other hand, is a framework that uses prior knowledge and information to update beliefs about a parameter or hypothesis, and provides probability distributions for parameters.
The main difference is that Bayesian statistics incorporates prior knowledge and beliefs into the analysis, while frequentist statistics doesn't.