Final answer:
The assertion that smaller sample sizes yield more accurate estimates is false; a larger sample size is generally more accurate and reliable.
Step-by-step explanation:
The statement that the smaller the sample size, the greater the accuracy of estimates from a random sample is false. The accuracy of sample estimates improves with a larger sample size. The central limit theorem explains that with larger samples, the sampling distribution of the sample means becomes approximately normal, which increases the accuracy of our estimates. A smaller sample size leads to more variability and a wider confidence interval, indicating a less precise estimate of the population parameter.
When conducting experiments, a larger sample size is generally more advantageous. It minimizes sampling error and increases the reliability of statistical estimates. In hypothesis testing involving matched or paired samples, it is important that two measurements come from the same pair of individuals or objects and that two-sample means are compared, as outlined in options B and C.