How does sample size affect statistical significance?

How does sample size affect statistical significance?

Sample size plays a crucial role in determining statistical significance. A larger sample size generally leads to more reliable results and a higher chance of detecting a true effect if it exists.

Why is a larger sample size better?

A larger sample size reduces the margin of error and increases the power of a statistical test, making it easier to identify significant differences.

Can a small sample size lead to false positives?

Yes, small sample sizes can increase the risk of Type I errors, where you might incorrectly conclude that there is a significant effect when there isn't one.

What is the ideal sample size for most studies?

The ideal sample size depends on various factors, including the study design, the expected effect size, and the desired power level, but generally, larger is better.

How can I determine the right sample size for my research?

You can use power analysis to determine the appropriate sample size based on your study's parameters, such as effect size and significance level.