What is the role of sample size in sequential testing?

Can you explain the role of sample size in sequential testing?

Sure! Sample size in sequential testing is crucial because it determines the reliability of the results. A larger sample size can provide more accurate estimates and reduce the risk of Type I and Type II errors.

Why is it important to minimize errors in testing?

Minimizing errors is vital because it ensures that the conclusions drawn from the test are valid. High error rates can lead to incorrect decisions, which can be costly in fields like medicine or product development.

How does sequential testing differ from traditional testing?

Sequential testing allows for ongoing analysis of data as it is collected, which can lead to earlier decisions. Traditional testing typically waits until all data is collected before analysis.

What factors should I consider when determining sample size?

You should consider the desired power of the test, the significance level, the expected effect size, and the variability in the data.

Can you give an example of how sample size affects outcomes?

Certainly! In a clinical trial, a small sample size might miss a significant treatment effect, while a larger sample size can reveal the true effectiveness of the treatment.