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the 1000 hypotheses tested, 10% are in fact true.https://scientificallysound.org/wp-content/uploads/2017/10/fig3.pngfig3With alpha set to 0.05, 5% of the 900 hypotheses that are not true will have p-values below 0.05. Thus, 45 tested hypotheses will be statistically significant when they are in fact not true (i.e., false-positives).https://scientificallysound.org/wp-content/uploads/2017/10/fig4.pngfig4With beta = 20%, 80% of the hypotheses that are true will have p-values below 0.05. These are true positives. This also means that 20% of the hypotheses that are true will have p-values above 0.05; these are false-negatives. With 100 true hypotheses, 80 of them will be statistically significant. https://scientificallysound.org/wp-content/uploads/2017/10/fig5.pngfig5With beta = 80%, 20% of the hypotheses that are true will have p-values below 0.05. These are true positives. This also means that 80% (!) of the hypotheses that are true will have p-values above 0.05; these are false negatives. With 100 true hypotheses, only 20 of them will be found to be statistically significant. When we also consider that we will also discover 45 false positive effects (5% of 900), the false-positive report probability will be 69%https://scientificallysound.org/wp-content/uploads/2017/10/fig6.pngfig6With beta = 60%, 40% of the hypotheses that are true will have p-values below 0.05. These are true positives. With 500 true hypotheses, 200 of them will be found to be statistically significant. Given that alpha = 0.05, 5% of the hypotheses that are false will be statistically significant. This means there will be 25 false-positives. Thus, the false-positive report probability will be 11%.https://scientificallysound.org/wp-content/uploads/2017/10/fig7.pngfig7With beta = 60%, 40% of the hypotheses that are true will have p-values below 0.05. These are true positives. With only 10 true hypotheses, 4 of them will be found to be statistically significant. Given that alpha = 0.05, 5% of the hypotheses that are false will be statistically significant. This means there will be 50 false positives. Thus, the false-positive report probability will be 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