The goal of hypothesis testing is usually to correctly reject the null hypothesis, or in other words, to show that the treatment applied has had an effect. The probability of correctly rejecting the null hypothesis is called the power of a statistical test. Statistical power is large when the treatment effect is large. Put another way, you are more likely to correctly reject the null hypothesis when the treatment has created a large difference between your sample data and the original population. Statistical power is greater for one-tailed tests.
Larger samples provide greater power.
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