Sample Estimation Computation

The bias of an estimator refers to how reproducible the estimates are over repetitions of the process.

A census has no sampling error because there's only one possible sample that can be generated.

The reliability of an estimator can be measured in terms of its standard deviation.

In a census, nonsampling errors are easier to manage compared to sampling errors.

Selection error occurs when the sample chosen is not a true representation of the population.

Response bias refers to errors caused by the instrument used for measurement.

If the computed estimates from sample to sample are very different from each other, we expect the sampling error to be large.

In actual practice, we can determine the exact difference between the estimate and the parameter because the value of the parameter is known.

It is impossible to estimate how varied the estimates are from sample to sample and how close they are, on average, to the parameter of interest.

Nonsampling errors can only arise from one specific source and do not need to be controlled.

There is a simple and direct method for estimating the size of nonsampling errors.

Variability in measurements can affect the size of sampling error introduced in the study results.

A smaller standard error of an estimator indicates higher reliability.

The accuracy of an estimator is typically assessed using its MSE.

If an estimator has a bias of 0, it is referred to as a biased estimator.

For unbiased estimators, reliability is equivalent to accuracy.

The variance of an estimator is used to measure reliability.

MSE is calculated as the squared difference between the estimator and the true parameter.