Final answer:
A large sample size encourages precision by increasing the likelihood that the sample is representative of the population and by reducing chance errors and sampling variability. It does not promote bias, decrease accuracy, or increase the standard error.
Step-by-step explanation:
A sample should be large to encourage precision. An increased sample size generally leads to a higher likelihood of the sample being representative of the population. Larger sample sizes reduce chance errors and make confidence intervals narrower, thereby increasing precision and accuracy.
Larger samples do not encourage bias; instead, the randomness and method of selection are what affect bias. Thus, a large sample still needs to be randomly selected to avoid bias. Additionally, larger samples lead to a smaller standard error, not a large one, as they help reduce the sampling variability, and therefore, improve the reliability of the statistic.
Regarding the question about the true effect of changing the sample size, bigger samples lower the error bound and make the confidence interval more precise. However, it should be noted that large samples can be biased if not chosen correctly, and hence size alone does not ensure a lack of bias.