Can linear regression be used in cases of sample bias?

Linear regression can be used when there is sampling bias, but it is important to understand the consequences and limitations.

Sampling bias means that the data used to train the model are not representative of the entire population. As a result, the model may produce systematically biased predictions.

When using linear regression on biased data:

• Coefficient estimates may be biased, leading to incorrect conclusions about feature effects.
• The model may perform poorly on new data that differ from the training set.

To minimize the impact of bias:

• Use sampling correction methods, such as weighting observations.
• Apply data collection techniques that reduce bias.
• Consider more complex models or methods that are robust to bias.

Thus, linear regression is applicable, but results should be interpreted with caution and, if possible, data or the model should be adjusted.