We have our null hypothesis, we have our alternative hypothesis. What we do is we look at the population, the population size, there's 40 employees on staff at this company. We take a sample, in Jules' case, she took a sample size of 10, and then we calculate a sample statistic, in this case it is the sample proportion, which is equal to, let's just call it p hat sub one. And then we want to calculate a p value. And just as a bit of review, a p value is the probability of getting a result at least as extreme as this one if we assume our null hypothesis is true. And in this particular case, because she suspects that not 10% are getting the exceeds expectations, this would be the probability of your sample statistic being less than or equal to the one that you just calculated for a sample size of n equals 10 given that your null hypothesis is true. And if this p value is less than your predetermined significance level, maybe that's 5% or 10%, but you'd want to decide it ahead of time, then you would reject, you would reject your null hypothesis because the probability of getting this result seems pretty low, in which case it would suggest the alternative.