Larger sample sizes lead to wider confidence intervals. narrower confidence intervals. shorter hypothesis tests. more assumptions about data.

Larger sample sizes lead to narrower confidence intervals! This is because with more data points, we have a better estimate of the population parameter, and this increased precision reduces the uncertainty around our estimate. Think of it like trying to hit a target—you’re more likely to hit the bullseye when you have more arrows to shoot! Additionally, when you conduct hypothesis tests, a larger sample size increases the statistical power of the test. This means you're more likely to detect a true effect or difference when one actually exists. Imagine having a magnifying glass: the bigger the sample size, the clearer the view you have of what’s really happening!