The role of sample size in hypothesis testing is crucial, as it affects the accuracy and reliability of the test results. A larger sample size generally leads to more precise estimates of population parameters, reduces variability, and increases the power of the test, making it more likely to detect a true effect or difference when it exists; conversely, a small sample size may result in a higher chance of Type I and Type II errors and can lead to inconclusive or misleading results.

Sample size plays a important role in hypothesis testing. Larger samples can lead to more accurate estimates of population parameters, reduce variability, and increase the power of the test, making it more likely to detect a true effect if one exists.