 We're back in our one-line hypothesis test video. In this video, we're going to focus on the hypothesis test for a single proportion. Here, our null hypothesis is that the proportion is equal to 50 percent, and our alternative is that it's less than that. In effect, looking at how frequently wind generation fell below capacity during the Texas 2021 cold snap. In our previous lesson, we went over how to do the randomization procedure for this test. We start off with simulating random data using random binomial. Then we calculate the original or the sampling distribution of proportions, calculate the data sample, and then we plotted it and eventually got a p-value equal to zero, which led us to reject the null hypothesis that the wind actually fell below capacity less than 50 percent of the time. If we want to do the one-liner test for this, it involves a couple of extra steps. We're still going to be using that stats.cipi library. But before we can actually implement the test, we need to define two variables. The first is success rate. This is how often we were successful. Here, success rate is defined by the length of wind, which wind was above 6.1 above capacity. Then we need to define the sample size, which is just the total number of variables or data points. Then our actual one-line test is stats.binome underscore test, so binomial test. We give it x equals our success rate, n equals our sample size, and again, we specify the alternative equal to less. If you were doing a right-tailed test, you would change this to greater, for example. But in this case, following what we did up here, we're going to do a left-tailed test. We can run this. We've got a warning that this command is currently being phased out, so eventually we'll need to use this new command, binome test. But for now, it's still working, and essentially it automatically prints out this p-value here. And so because of that, we can compare to this and look that they're very similar. Once again, our one-line test is showing a bit more specificity in that number, but it's still very, very close to zero. So again, we reject the hypothesis.