Explanation:
In statistics, power refers to the probability of correctly rejecting the null hypothesis (H0) when it is actually false. In other words, it is the ability of a statistical test to detect a true effect or difference.
Power is a complement to the Type II error rate, which is the probability of failing to reject the null hypothesis when it is actually false. Power can be increased by increasing the sample size, increasing the effect size, or decreasing the level of significance (alpha).
The desired power of a test is usually set at a pre-determined value, such as 0.8 or 0.9, to balance the trade-off between the risk of a Type II error and the sample size required to achieve the desired power.
In summary, power in statistics is a measure of the ability of a statistical test to detect a true effect or difference and is an important consideration in the design and interpretation of statistical experiments.