Which of the following methods can increase statistical power?

Increasing statistical power is crucial for detecting true effects in hypothesis testing, and several methodologies can effectively contribute to enhancing power.

Increasing sample size is one of the most direct and impactful ways to boost statistical power. A larger sample size reduces the standard error, making it easier to detect an effect if one truly exists. This is because a larger sample means more information about the population, thereby leading to more accurate estimates of parameters and a greater likelihood of observing significant results.

Controlling variables is also important. By controlling for extraneous variables, researchers can reduce noise in the data, which can obscure true effects. This helps clarify the relationship between the independent and dependent variables, thereby increasing the likelihood of finding significant results.

Increasing homogeneity, or reducing variability within groups, can also lead to increased power. When groups are more similar, the variability in the measurements is reduced, which can help in more precisely estimating the impact of the treatment. This leads to a lower standard error and, consequently, a higher chance of identifying an effect.

In summary, each of these methods contributes to increasing statistical power, and thus, the correct answer encompasses all these strategies. By utilizing them, researchers can enhance the effectiveness and reliability of their findings in evidence-informed practice.