In Statistics, in an experimental process, an experiment has some randomness to it. When we repeat the experiment again and again, there is a chance that we may get slightly different data each time. When we run a statistical estimator over this data, we might get slightly different estimation each time. In statistics we take the true value of the parameter as a constant and the experimental estimate as a probabilistic variable. Bias or Bias in statistics is the systematic, or average difference between the true value of the parameter (constant) and the experimental estimate (probabilistic variable) value
Let us suppose that the police decided to estimate the average speed of the drivers on the fast lane in a freeway. Considering how it can be done, they felt they can follow a method, in which they follow the cars using police patrol cars and record their speeds as the speed of the car would be the same as that of the police car. This is where bias comes into picture, by using this method the police might end up in a biased result, as the driver of the car who is speeding might become conscious on seeing a police car behind him and will slow down to get back to the speed limit allowed.
By the above example we can Define Bias as the term which refers to how far the average statistics lies from the parameter it is estimating, which is the error which arises while estimating a quantity.Bias in statistics refers to directional error in an estimator. Statistical bias is error we cannot correct by repeating the experiment many times and finding the average of the results.
Bias in normal usage is an inclination or preference that influences a judgment from being balanced, in statistics Definition for Bias is the systematic error which occurs when the expected (estimated) value is different from the true value
In statistics we come across two types of errors, random errors and systematic errors. Random error is the error due to sampling variability or at times measurement precision. It occurs essentially in all quantitative studies and can be minimized to an extent but not avoided. Systematic error or Bias is a reproducible inaccuracy that produces a consistently false pattern of differences between observed (estimated) and true values. For us Bias Definition is nothing but the systematic or average difference between the true value of the parameter and the experimental estimate value.
