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# P-value calculation with two opposite Null hypothesis

Super Learner
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Hi,
I’ve asked this question before, but I didn’t get an answer, so I try to clarify my problem. In the hypothesis testing section in the video I linked below there is this 40% email open rate example. We want to know whether our competitor’s open rate is higher than that of ours so we create the following hypotheses: H0: Mean(or) <= 40%, and H1: Mean(or) > 40%. Then we get a T-score of -0.53 and a critical value of 1.83 so we accept the H0. So far, so good.
What if we wanted to know whether our competitor’s open rate is lower that that of ours? Then H0: Mean(or) >= 40%, and H1: Mean(or) < 40%. H0 is the opposite, but we have the exact same database, exact same sample mean, standard deviation, standard error, T-score and critical value. Again we accept the H0. This is a paradox to me.
In both cases we accept the H0 in spite of the fact that they are the exact opposite of each other. So my question is the following: What am I missing here? How does the calculation change if we ask the opposite question, therefore have an opposite H0.
GB
https://365datascience.teachable.com/courses/233979/lectures/3649566