Which of the following methods can increase statistical power?

Increasing statistical power in hypothesis testing refers to the likelihood of correctly rejecting a false null hypothesis. In this context, all three methods mentioned can contribute to enhancing statistical power.

When the alpha level (α) is increased, this effectively raises the threshold for statistical significance. A higher alpha level means that the test becomes more liberal, allowing more potential to identify an effect if it exists, which increases the power of the test. However, this comes with the trade-off of a higher risk of committing a Type I error (falsely rejecting a true null hypothesis).

Increasing the sample size is one of the most effective ways to enhance statistical power. A larger sample provides more information and helps to more accurately estimate the population parameters, leading to a clearer distinction between the null and alternative hypotheses. This increased precision decreases the variability of the sample statistic, which in turn increases the likelihood of detecting an effect if it’s present.

Increasing the effect size (γ), which refers to the magnitude of the difference between groups or the strength of a relationship, directly impacts power. A larger effect size makes it easier to detect a difference or a relationship because it becomes more pronounced against the backdrop of any variability. When effect sizes are larger, the statistical tests have a greater chance of identifying