Final answer:
The statement is false; in a statistical context, power cannot be increased directly like turning up a knob. To address low power, statisticians increase the sample size while keeping the significance level (alpha) constant, thus reducing the risk of a Type II error.
Step-by-step explanation:
The statement is false. If the surgeon reports they are not getting enough power, this situation is analogous to a statistical issue where power refers to the probability of correctly rejecting a null hypothesis when it is indeed false. In statistics, when confronted with a scenario where the power is too low, the appropriate response is not to simply 'increase the power' as if it were a knob that could be adjusted. Instead, statisticians will typically choose to increase the sample size while keeping the significance level (alpha) the same. A low power means there's a higher risk of committing a Type II error, where the null hypothesis is not rejected when it should be. Therefore, increasing the sample size can improve the power of the test without changing the statistical thresholds.