going to improve your statistical significance for the most part. So what you need to do is you need to do what you do with weather forecast. You'd basically check against what actually happened or in our case what humans actually said. And that's where we're going to basically be measuring distributions of data. Unlike classic e-vals where there's clearly a right and wrong and you can score how many were right and how many wrong, now we need to measure the data as a comparison of distributions. And there are many ways for distributions to get wrong. They could be completely wildly off. They can as we mentioned get the average right but the shape of the distribution wrong. And so you're going to need multiple metrics to capture how well your model is reflecting different personas. Um I recommend using a correlation type metric along with one of these shape type metrics which capture what the underlying shape of the distribution is. The other thing you need to do is estimate the fundamental noise in your ground truth data. That experiment I talked about in the beginning where they got 83% accuracy, the key smart thing they did is they took those humans and they brought them back 2 weeks later and they redid the battery of surveys and personality tests and they found that the humans on average were only 80% consistent to themselves. So that sets a noise floor as how accurate our models could ever get because the humans themselves are fundamentally noisy. And so the 83% is actually normalized against that. If you can do this and bring your humans back, that's great. Very often you can't. So the way you can kind of artificially do this is take your ground truth human data, break it into two chunks, and then pretend one is synthetic and one is human, and then measure the correlation and repeat that hundreds and thousands of times and average it, and that'll set kind of a noise floor that your ground truth data where half of it's synthetic, half of it real, could be the level of accuracy you could hope to get. So hopefully by now you have an