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When we deal with a population, most of the time the parameters are unknown. So we cannot draw any conclusion about the population. To know the unknown parameters the technique is to draw a sample from the population and try to gather information about the parameter through a function which is reasonably close. Thus the obtained value is called an estimated value of the parameter, the process is called estimation and the estimating function is called estimator.

A good estimator should satisfy the four properties which we briefly explain below :

(a) Un Biasedness. A statistic t is said to be an unbiased estimator of a parameter θ if,

E [t] = θ.

Otherwise it is said to be 'biased'

Theorem 1. Prove that the sample mean x is an unbiased estimator of the population mean µ

Proof. Let xl,x2, …,x, be a simple random sample with replacement from a finite population of size N, say, X1, X2,… , XN


                                                µ = (X1+X2+….+ Xn)/N          

To provethat                                       E(x) = µ


While drawing xi, it can be one of the population members i.e., the probability distribution of Xi can be taken as follows :


The same result is also true for infinite population and the sampling without replacement.


Theorem 2. The sample variance

is a biased estimator of the population variance σ2.


Proof. Let x1, x2, ... , xn be a random sample from an infinite population with mean σ and

variance σ 2.

Then                E (x) = µ, Var (xi) = E (xi - µ)2 = σ 2,                        for i = 1, 2, ... , n.

ð  s2 is a biased estimator of σ2


Thus s2 is an unbiased estimator of σ2.

Example 1. A population consists of 4 values 3, 7, 11, 15. Draw all possible sample of size

two with replacement. Verify that the sample mean is an unbiased estimator of the population mean.

Solution. No. of samples = 42 = 16, which are listed below:

(3, 3), (7, 3), (11, 3), (15, 3)

(11 , 7), (15, 7), (11 , 11), (15, 11)

(11, 15), (15, 15), (3, 7), (7, 7),

(3, 11), (7, 11), (3, 15), (7, 15)

Population mean, 

Sampling distribution of sample mean

Sample mean () Frequency     f () .f ()


























Mean of sample mean = 144 / 16= 9

Since, E () = µ,

ð  Sample mean is an unbiased estimator of the population mean.


(b) Consistency. A statistic tn obtained from a random sample of size n is said to be a consistent estimator of a parameter if it converges in probability to θ as n tends to infinity.

Alt, If E [Tn]  θ and Var [Tn]  0 as n ∞, then the statistic tn is said to be consistent estimator of θ.

For example, in sampling from a Normal Population N (µ, σ2),

Hence the sample mean is a consistent estimator of population mean.


(c) Efficiency. There may exist more than one consistent estimator of a parameter. Let T1 and T2 be two consistent estimators of a parameter θ. If

Var (T1) < Var (T2) for all n

then T1 is said to be more efficient than T2 for all sample size.

(d) Sufficiency. Let x1, x 2, .. . , xn be a random sample from a population whose or pdf is f (x, 8). Then T is said to be a sufficient estimator of e if we can express the following :


                        f (x1,θ) . f(x2, θ)…..f(xn, θ) = g1(T , θ). g( x1,x2,…, xn)


where g1 (T, e) is the sampling distribution of and contains 8 and g2 (x1,x2,…, xn ) is independent of θ.


Sufficient estimators exist only in few cases. However in random sampling from a normal population, the sampling mean x is a sufficient estimator of µ.