# Does bias have multiple meanings in Data Science?

• What are the meanings of Bias?

• And is Under fitting, which is used in machine learning contexts, the same as "Bias"?

I have faced biased data in sampling in statistics but it seems this is a different thing to bias in learning concepts.

I have heard that some data sets are biased, also have heard the model (for example neural network) has low bias or e.g. 'high bias' problem. Are these uses of bias different?

Bias can mean different things in statistics:

• If your model is biased, it's likely your model is under-fitting.
• Some data set is biased in sample collection. For instance, if you assume your sample responses are independent, but somehow it's not, this is a bias in your data set. If you want to sample everybody in the country, but you skip some cities for no reason, this causes bias in your data set.
• Your estimators could be biased - the expectation of your estimator is not equal to the true value in the population.
• "Bias" is also used to describe an learnable offset parameter when using transfer functions, e.g. in neural networks when calculating activation of an artificial neuron.