What is the impact of sample size on p-value in statistical tests?

What is the impact of sample size on p-value in statistical tests?

The sample size can significantly affect the p-value. Larger sample sizes tend to provide more reliable estimates of the population parameters, leading to more accurate p-values.

Can you explain why larger sample sizes lead to more accurate p-values?

Certainly! Larger sample sizes reduce the standard error, which means the sample mean is closer to the true population mean. This results in a more precise p-value.

Does that mean smaller sample sizes are always bad for statistical tests?

Not necessarily. Smaller sample sizes can still provide useful insights, but they may lead to less reliable p-values and a higher chance of Type I and Type II errors.

How can I determine the appropriate sample size for my study?

You can determine the appropriate sample size using power analysis, which considers the expected effect size, significance level, and desired power of the test.