Question

Explain the difference between ‘sampling bias and ‘sampling error’, and a how a researcher would decrease sampling error and sampling bias. Explain why reducing each is important for testing a hypothesis.

Answer 1

Sampling bias occurs when a sample statistic does not accurately reflect the true value of the parameter in the target population. Research is bias when it is gathered in a way that makes the data’s value systematically different from the true value of the population of interest.

Sampling error is the error caused by observing a sample instead of the whole population. The sampling error is the difference between a sample statistic used to estimate a population parameter and the actual but unknown value of the parameter.

One of the most effective methods that can be used by researchers to avoid sampling bias is simple random sampling, in which samples are chosen strictly by chance. This provides equal chances for every member of the population to be chosen as a participant in the study at hand.

Sampling errors vanish if observations cover the complete population.

Under these circumstances, the sample does not truly represent the population of interest. Sampling error and sampling bias both can affect inferences based on sampling.