A consistent estimator is a statistical method that produces an estimate that becomes more accurate as the sample size increases. In other words, it consistently approaches the true value of the population parameter.
Imagine you are trying to hit a target with darts. At first, your throws might be scattered all over the place and not very accurate. But as you keep throwing more and more darts, your aim improves and gets closer to hitting the bullseye. Similarly, a consistent estimator gets better at estimating the true value as you increase your sample size.
Unbiased Estimator: An unbiased estimator is one that, on average, gives an estimate equal to the true value of the population parameter.
Efficiency: Efficiency refers to how precise or how close an estimator's estimates are to each other.
Sampling Variability: Sampling variability refers to the natural variation in estimates that occurs when different samples are taken from the same population.
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