a) Increasing the size of the sample generally results in a narrower confidence interval. This means that as the sample size increases, the estimate of the population parameter becomes more precise, and we can be more confident that the true population parameter lies within our estimated confidence interval.
b) The P-value of a test is the probability that the observed result (or more extreme) could have occurred by chance alone, assuming the null hypothesis is true. As the sample size increases, this probability decreases, because with a larger sample size, we have more statistical power to detect a difference between the sample mean and the population mean, if such a difference exists. Therefore, as the sample size increases, the P-value of a test decreases, making it less likely that the observed result occurred by chance alone.