A characteristic that describes a sample is called a statistic, and a characteristic that describes a population is called a parameter. Although sample statistics are usually representative of corresponding population parameters, there is typically some discrepancy between a statistic and a parameter. The naturally occurring difference between a statistic and a parameter is called sampling error.
The Correct Answer and Explanation is :
The correct term for the naturally occurring difference between a statistic and a parameter is sampling error.
Explanation:
Sampling error arises due to the inherent differences between a sample and the entire population from which it is drawn. A statistic is a characteristic or measure that is calculated from a sample, whereas a parameter is a characteristic or measure that describes an entire population. When we collect data from a sample, the goal is often to make inferences about the population as a whole, but it’s important to recognize that there is typically some discrepancy between the two.
Sampling error occurs because the sample is only a subset of the population. It is possible that the sample does not perfectly represent the entire population. Even with a random sample, which is ideally designed to minimize bias, the sample might still miss certain characteristics of the population or over-represent others. For instance, in a sample of students from a school, the distribution of ages, genders, or ethnicities may not match the distribution in the full population, leading to variations in statistics such as the mean, median, or proportion.
One of the key things to note is that sampling error is a natural and unavoidable part of statistical analysis. The larger the sample size, the smaller the sampling error tends to be. This is because larger samples are more likely to reflect the diversity and characteristics of the full population. However, even with large samples, sampling error can still occur, though it’s typically smaller.
Understanding sampling error is crucial for interpreting statistical results accurately. For example, when polling for political elections, there will always be some sampling error, which is why poll results are often presented with a margin of error. This margin gives an estimate of the sampling error and helps to convey how much the sample statistic might differ from the true population parameter.