How does sample size affect confidence intervals?

How does sample size affect confidence intervals?

The sample size directly impacts the width of confidence intervals. A larger sample size generally leads to a narrower confidence interval, indicating more precise estimates.

Why is a narrower confidence interval better?

A narrower confidence interval means that we have more confidence that the true population parameter lies within that range, leading to more reliable conclusions.

Can you give me an example?

Sure! If you have a sample size of 30, your confidence interval might be 5-15. With a sample size of 300, it could narrow to 8-12, providing a more precise estimate.

What happens if the sample size is too small?

If the sample size is too small, the confidence interval may be too wide, making it difficult to draw meaningful conclusions about the population.

How do I determine the right sample size for my study?

Determining the right sample size involves considering the desired confidence level, margin of error, and the variability in the population.