Consider fitting a straight line for the relationship of an outcome variable "y" to a predictor variable "x", and estimating the slope of the line. Statistical variability, measurement error or random noise in the "y" variable causes uncertainty in the estimated slope, but not bias: on average, the procedure calculates the right slope. However, variability, measurement error or random noise in the "x" variable causes bias in the estimated slope (as well as imprecision). The greater the variance in the "x" measurement, the closer the estimated slope must approach zero instead of the true value.