Question

What is an unbiased estimator? Try to use own words describing this method or use an example to describe it.

Answer 1

A statistic is said to be an unbiased estimate of a given parameter when the mean of the sampling distribution of that statistic can be shown to be equal to the parameter being estimated. For example, the mean of a sample is an unbiased estimate of the mean of the population from which the sample was drawn.

For example, to make things as unbiased as possible, judges of an art contest didn't see the artists' names or the names of their schools and hometowns. You are unbiased if you can assess situations with a completely open mind.

In other word,

An unbiased estimator is an accurate statistic that’s used to approximate a population parameter. “Accurate” in this sense means that it’s neither an overestimate nor an underestimate. If an overestimate or underestimate does happen, the mean of the difference is called a “bias.”

In more mathematical terms, an estimator is unbiased if:

That’s just saying if the estimator (i.e. the sample mean) equals the parameter (i.e. the population mean), then it’s an unbiased estimator.
You might also see this written as something like “An unbiased estimator is when the mean of the statistic’s sampling distribution is equal to the population’s parameter.” This essentially means the same thing: if the statistic equals the parameter, then it’s unbiased.