Final answer:
Sample size and sampling error are inversely proportional; as one increases, the other decreases.
Step-by-step explanation:
The relationship between sample size and sampling error is best described as inversely proportional. This means that as the sample size increases, the sampling error tends to decrease, and vice versa. Sampling error refers to the discrepancy that may occur between the sample statistics and the actual population parameters. With a larger sample size, the sample is more likely to represent the population accurately, thus reducing the potential error. If we were to represent this relationship graphically, as sample size increases on the x-axis, the sampling error on the y-axis would decrease, resembling a hyperbola, as shown in example (c) for an inverse relationship.