 So before we can go in and calculate the p-value, the way that we have in the past, we need to convert this array into a data frame. And this will help with visualization later. And so we can say chi-square vf is just pd dot data frame of chi-square and our columns, we'll just call them chi-square. So we can run that. And then when we go to actually calculate our p-value, it is the length of chi-square df in which chi-square df on the chi-square column is less as greater than or equal to chi-square Sam and then divided by n. So we've got a erroneous space there. And so we can print this and we can see that our p-value is 0.176. And so we can write a conclusion. We can say that the p-value is greater than 0.05, which is our pretty standard significance value. So we fail to reject or we can say that we accept the null hypothesis. In other words, there is sufficient evidence to say that the proportions all equal 0.25. Or we can say that the proportions are as specified, which is 0.25. And this is how we can then include that chi-squared test.