Concept:
In statistical estimation, an estimator \( \hat{\theta} \) is said to be unbiased if its expected value equals the true population parameter \( \theta \). Mathematically, this is expressed as:
\[
E(\hat{\theta}) = \theta
\]
This means that on average, the estimator correctly estimates the true parameter value.
Step 1: Understand the definition of an unbiased estimator.
If an estimator \( \hat{\theta} \) satisfies
\[
E(\hat{\theta}) = \theta
\]
then the estimator does not systematically overestimate or underestimate the parameter.
Step 2: Identify the property.
The property that ensures the expected value of an estimator equals the population parameter is called Unbiasedness.
\[
\therefore \text{The correct answer is Unbiasedness.
\]