When we run an A/B test, we usually want to use the results of the test to make a decision: use version A or B? In order to make that decision, many data scientists use a pre-determined significance threshold for their hypothesis test. For example, if we set a significance threshold of 0.05 (a commonly chosen value), we’ll “reject the null hypothesis” and conclude that the conversion rate for version B is significantly different from version A if we get a p-value less than 0.05.

It turns out that this significance threshold is the *false positive rate* for the test: the probability of finding a significant difference when there really is none. As a business owner, we don’t want to make this kind of mistake, because then we might invest money in a change that doesn’t actually make a difference!

Unfortunately, there’s a trade-off between false positives and false negatives. A false negative occurs when there is a difference between version A and B, but the test doesn’t detect it. This is a potential missed opportunity for a business owner!

Most A/B test sample size calculators estimate the sample size needed for a 20% false negative rate; while a data scientist needs to choose the false positive rate they are comfortable with. The lower the false positive rate, the larger the sample size will need to be!

### Instructions

Try changing the significance threshold for the calculator in the workspace. Note how the sample size changes. Do you see how a lower threshold (lower false positive rate) requires a larger sample size?