Describe sampling error, making use of the following terms: sample, population, parameter, statistic.

Expert Solution

In statistics, sampling error is defined as the difference between population parameter and the sample statistic, which was used to estimate that parameter.

Population parameter describes the whole population , where as , a statistic describes a sample of observations taken from that population. So, naturally there is a difference between the two values due to multiple data collection process biases (like response bias, interviewer bias etc.)

For example, suppose owner of a group of schools wants to do a survey on which game is students preferring more between soccer, tennis and cricket. So, they selected a group of students and asked this question . Which results in 40% voted for soccer, 30% for tennis and 30% for cricket. And when they asked each of the students , it turned out to give a bit different results. It showed 50% liked soccer,20% liked tennis and 30% liked cricket.So,it can be seen that there is a small difference between the two results. This difference occurs due to sampling error.

Sampling error is unavoidable. But one thing to keep in mind is - "Larger the sample, smaller the error".

orchestra answered 2 years ago

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