Recently, the factory began a new production line that is more efficient than the existing production line. However, the factory still needs ball bearings to meet the same specifications. To compare the accuracy of the new process against the existing process, the factory decides to take two random samples of ball bearings. The first sample is of 50 randomly selected ball bearings from the existing production line, and the second sample is of 50 randomly selected ball bearings produced from the new production line. For each sample, the diameters of the ball bearings were measured.

Diameters data frame of the first sample (showing only the first five observations)

diameters

02.36

12.76

22.79

32.42

42.57

Diameters data frame of the second sample (showing only the first five observations)

diameters

02.90

13.23

2-0.19

32.24

41.55

Suppose that the factory claims that the proportion of ball bearings with diameter values less than 2.20 cm in the existing manufacturing process is the same as the proportion in the new process. At alpha=0.05, is there enough evidence that the two proportions are the same? Perform a hypothesis test for the difference between two population proportions to test this claim.

1.Define the null and alternative hypotheses in mathematical terms as well as in words.

2.Include the test statistic and the P-value. See Step 2 in the Python script. (Note that Python methods return two tailed P-values. You must report the correct P-value based on the

alternative hypothesis.)

test-statistic = -2.5 two tailed p-value = 0.0124   

I'm just confused about the second, I worked with this before, but the P-value in the alternative hypothesis is no working for me, please help.

how can I get to the conclusion?