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Statistical power is the probability of correctly rejecting a false null hypothesis (1 – β). Power increases with larger effect sizes, higher alpha, lower variance, and larger sample sizes. Among the options, increasing sample size guided by an a priori power analysis is the most direct and ethically defensible way to reduce Type II error while planning the study. Lowering alpha (Option A) decreases the chance of Type I error but also reduces power, increasing Type II error risk. Switching from one-tailed to two-tailed tests (Option C) is more conservative and typically reduces power unless the direction of effect is uncertain; power gains occur when moving from two-tailed to one-tailed (with justified directional hypotheses). Dichotomizing continuous outcomes (Option D) generally reduces statistical power by discarding information and increasing measurement error; continuous measures are typically preferable. A priori power analysis integrates expected effect size (from prior studies or pilot data), desired alpha, and planned power (often .80 or .90) to yield a required sample size, supporting robust inference and ethical resource use.