1.1 1. A/B Testing A/B testing is a form of hypothesis testing that allows you to make comparisons between two distributions You'll almost never be explicitly asked to perform an A/B test. Make sure you can identify situations where the test is appropriate and know how to correctly implement each step. Question 1.1 : The following statements are the unordered steps of an A/B hypothesis test : 1. Choose a test statistic (typically the difference in means between two categories ) 2. Shuffle the labels of the original sample , find your simulated test statistic , and repeat many times 3. Find the value of the observed test statistic 4. Calculate the p-value based off your observed and simulated test statistics 5. Define a null and alternate model 6. Use the p-value and p-value cutoff to draw a conclusion about the null hypothesis Make an array called ab_test_order that contains the correct order of an A/B test , where the first item of the array is the first step of an A /B test and the last item of the array is the last step of an A /B teat [3] : ab_test_order = make_array (5, 1, 3, 2, 4, 6) [4] : ok . grade ("q1_1 ") ; tests Test summary Passed : 1 Failed : 0 [ooooooooook 100.0 % passed Question 1.2: If the null hypothesis of an A/B test is correct , should the order of labels affect the differences in means between each group? Why do we shuffle labels in an A/B test? Write your answer here, replacing this test