What is sampling bias?

What is sampling bias?

• A. Bias caused when the sampled data is not representative of the population the model will be used on
• B. Bias caused when sampling is random
• C. Bias caused by data leakage
• D. Bias caused by overfitting

Answer: (A)

Sampling bias happens when the data you use for training or analysis doesn’t reflect the population the model will operate on. That mismatch means the model learns patterns that are true only for the biased sample, so its predictions don’t generalize well to the real population. For example, surveying only urban customers to gauge national preferences misses rural views and distorts results. Random sampling helps reduce bias but only if the sampling frame covers the whole population and response rates are balanced; otherwise bias can still creep in. In contrast, data leakage involves using information in training that wouldn’t be available in deployment, and overfitting is about the model fitting noise in the training data rather than issues with how the data was sampled.

Sampling bias happens when the data you use for training or analysis doesn’t reflect the population the model will operate on. That mismatch means the model learns patterns that are true only for the biased sample, so its predictions don’t generalize well to the real population. For example, surveying only urban customers to gauge national preferences misses rural views and distorts results. Random sampling helps reduce bias but only if the sampling frame covers the whole population and response rates are balanced; otherwise bias can still creep in. In contrast, data leakage involves using information in training that wouldn’t be available in deployment, and overfitting is about the model fitting noise in the training data rather than issues with how the data was sampled.