This is probably the most important property that a good estimator should possess. According to this property if the statistic  is an estimator of  will be an unbiased estimator if the expected value of   equals the true value of the parameter
                            i.e.
            Consider the following worked example

Example:
            Show that the sample mean  is an unbiased estimator of the population mean.
Solution:
            In order to show that  is an unbiased estimator, we need to prove that
                                              
            We have
                                                     
            Therefore,
                                                  
            From the rule of expectation, the expected value of a linear combination is equal to the linear combination of their expectations, we have
                                               
            Since  are each random variable, their expected value will be equal to the probability mean,
                                              
            Therefore,         
Hence  is an unbiased estimator of the population mean.