For , the ratio of the two sample likelihood functions is
For it is strictly increasing in . The Neyman-Pearson lemma therefore selects an upper tail:
Under the null, , so choose with this upper-tail probability at most . If exact size is required, randomize at the adjacent boundary value as needed. The rejection rule is the same for every , giving a uniformly most powerful test for the one-sided alternative.

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