P-Values

The null hypothesis says that there are no real trends in our data and that everything we’re seeing in your sample is just noise. We want to find a way to reject that.

If we can reject that, we can infer that there is a real difference and it’s likely to continue. For this reason, we need to find a way to reject the null hypothesis.

We’re going to reject the null hypothesis using something called a (p)-value. The (p)-value is the answer to the question if the null hypothesis was true.

For example, if a p-value is 0.02, we’re saying you could only get this result 2% of the time if the null was true. It’s essentially a measure of how rare or uncommon that result would be if the null was actually true.

This means that we can reject the null hypothesis because it is not likely to occur again.

The smaller the (p)-value, the more likely it is that we are able to reject the null hypothesis. Normally in statistics, anything less than 5% is our threshold for rejecting the null hypothesis.