Bias, in a statistical framework, means that an estimate of a parameter has an expected value that is not equal to the actual parameter value. The bias of an estimator can be evaluated with the mean squared error: $$MSE(\widehat{\theta}) = E[(\widehat{\theta} - \theta)^2]$$ which can be decomposed into the sum of the squared bias and the variance of an estimator.