Hypothesis tests lead to a ___ regarding the null hypothesis, while confidence intervals provide a___ for the parameter. range; decision rejection; estimate estimate; rejection

Hypothesis tests lead to a **rejection** regarding the null hypothesis, while confidence intervals provide an **estimate** for the parameter. Essentially, when conducting a hypothesis test, you are looking to determine whether there is enough evidence to reject the null hypothesis in favor of an alternative. On the other hand, confidence intervals help us capture the likely range of values for a population parameter, giving us a more nuanced view of uncertainty in our estimates. To make hypothesis testing and confidence intervals effective tools in your statistical toolbox, it’s crucial to select appropriate significance levels and sample sizes ahead of time. A common mistake is neglecting assumptions, such as normality in your data, which can lead to misleading results. Always double-check your assumptions to ensure your tests and intervals hold up under scrutiny!