So our 95% confidence, confidence interval, could write it like this, or you could say that it goes from 0.39 minus 0.23, so that'd be 0.16, so it goes from 0.16 until 0.39 plus 0.23 is going to be what? 0.62. Now what a 95% confidence interval tells us is that 95% of the time that we take a sample and we construct a 95% confidence interval, that 95% of the time we do this, it should overlap with the true population parameter that we are trying to estimate. But in this hypothesis test, remember, we are assuming that the true population parameter is equal to zero, and that does not overlap with this confidence interval. So assuming, let me write this down, assuming null hypothesis is true, null hypothesis is true, we are in the less than or equal to 5% chance of situations, situations, where, where beta not overlap, overlap, with 95% intervals. And the whole notion of hypothesis testing is you assume the null hypothesis, you take your sample, and then if you get statistics, and if the probability of getting those statistics or something even more extreme than those statistics is less than your significance level, then you reject the null hypothesis. And that's exactly what's happening here.