Select the true statement regarding the relationship between sampling error and sample size.a.)The sample size can affect the sampling error.b.)In order to decrease sampling error, you must decrease the sample size.c.)The smaller a sample size, the more accurate an estimate can be.d.)The standard error increases as the sample s

The true statement regarding the relationship between sampling error and sample size is a.) The sample size can affect the sampling error.

Here's why:

Sampling error is the difference between a population parameter and a sample statistic used to estimate it. For example, if we were to take a sample of people's heights in a city to estimate the average height of all people in the city, the difference between our estimate and the actual average height would be the sampling error.

The size of the sample can affect the sampling error because the larger the sample, the more likely it is to be representative of the population, and therefore the smaller the sampling error. Conversely, the smaller the sample, the less likely it is to be representative of the population, and therefore the larger the sampling error.

So, increasing the sample size can decrease the sampling error, and decreasing the sample size can increase the sampling error. This is why statement a.) is true.

Statements b.) and c.) are false because they suggest the opposite: that decreasing the sample size can decrease the sampling error, and that a smaller sample can provide a more accurate estimate.

Statement d.) is incomplete and therefore cannot be evaluated.