What type of errors occur when a sample is not representative of the population in AI?

• A. Statistical/Computational errors
• B. Logical errors in code
• C. Network latency errors
• D. Hardware failure errors

Answer: (A)

When the sample doesn’t reflect the population, you’re facing a bias issue that leads to biased estimates and inferences. In AI, this shows up as statistical errors: the data distribution used to train and test the model doesn’t match the real-world distribution, so the model learns patterns that don’t generalize. This can make predictions, calibrations, and performance metrics optimistic in development but poor in production because the model hasn’t seen the kinds of data it will encounter in the wild. It’s a statistical problem tied to how data represent the population, not a bug in code, a network delay, or a hardware fault. For example, training a model on a dataset with little diversity can cause it to perform poorly on underrepresented groups.