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A statistical significance test is intended to test a hypothesis. If the hypothesis summarizes a set of data, there is no value in testing the hypothesis on that set of data. Example: If a study of last year's weather reports indicates that rain in a region falls primarily on weekends, it is only valid to test that null hypothesis on weather ...
Test errors can be false positives (the test is positive, but the virus is not present) or false negatives, (the test is negative, but the virus is present). [179] In a study of over 900,000 rapid antigen tests, false positives were found to occur at a rate of 0.05% or 1 in 2000.
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