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1. Of hypotheses interesting enough to test, perhaps 1 in 10 will be true. So imagine tests on 1,000 hypotheses, 100 of which are true.
2. The tests have a false positive rate of 5%. That means they produce 45 false positives (5% of 900).
They have a power of 0.8, so they confirm only 80 of the true hypoteses, producing 20 false negatives.
3. Not knowing what is false and what is not, the researcher sees 125 hypoteses as true, 45 of which are not.
The negative results are much more reliable - but unlikely to be published.

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