Five main factors increase the power of a significance test: larger sample size, larger effect size, higher significance level (alpha), lower variability in the data, and using a one-tailed test instead of a two-tailed test. Power is the probability that your test will correctly detect a real effect when one exists, calculated as 1 minus the probability of a Type II error. The widely accepted target is 0.80, meaning an 80% chance of catching a true effect.
Understanding what drives power up or down helps you design stronger studies and interpret results more critically. Here’s how each factor works.
Sample Size Has the Biggest Practical Impact
Increasing your sample size is the most straightforward way to boost power, and it’s the factor researchers have the most control over. A larger sample reduces the standard error of your estimate, which makes it easier to distinguish a real effect from random noise. The relationship is dramatic at smaller sizes and tapers off: when the effect you’re looking for is moderate, going from 8 to 30 participants can take your power from inadequate to strong. But if the effect is very small (a Cohen’s d of 0.2, for example), even 30 participants won’t get you to the 0.80 threshold.
This is why researchers run power analyses before collecting data. Tools like G*Power let you plug in your expected effect size, your chosen significance level, and your desired power level, then calculate the minimum sample size you need. Running this calculation during the planning stage is the single best way to avoid an underpowered study.
Larger Effect Sizes Are Easier to Detect
Effect size is the magnitude of the difference or relationship you’re trying to find. A drug that cuts symptoms in half is easier to detect than one that reduces them by 5%. When effect size is large (a Cohen’s d of 0.8 or above), you need surprisingly few participants. With an effect size of 2.5, as few as 8 samples can reach power of roughly 0.80. When the effect size drops to 1.0, you need closer to 30. At 0.2, you may need hundreds.
You can’t always control the effect size itself, since it depends on the real-world phenomenon you’re studying. But you can make design choices that preserve it. Using more precise measurement instruments, selecting a more homogeneous sample, or choosing a stronger dose or intervention can all effectively increase the effect your test has to work with.
A Higher Significance Level Increases Power
The significance level, alpha, is the threshold you set for calling a result statistically significant. Most studies use 0.05, meaning a 5% chance of a false positive. If you raise alpha to 0.10, you widen the rejection region, making it easier to reject the null hypothesis. That directly increases power.
The tradeoff is straightforward: a larger alpha means a higher risk of a Type I error (concluding there’s an effect when there isn’t one). Lowering alpha to 0.01 makes your test more conservative but less powerful. In practice, 0.05 is the default in most fields, but understanding this lever matters. If you’re running a preliminary screening study where missing a real effect is costlier than a false alarm, a slightly higher alpha may be justified.
Lower Variability Sharpens the Signal
When the data within your groups are all over the place, it’s harder to tell whether differences between groups are real. Reducing this variability, often called within-group variance or error variance, increases the precision of your estimates and boosts power.
Several practical strategies reduce variability. Standardizing your measurement procedures cuts down on random error. Using more reliable instruments helps. Controlling for confounding variables (through matching, blocking, or statistical adjustment) removes noise that would otherwise obscure the effect you’re looking for.
Repeated Measures Designs
One of the most effective ways to reduce variability is using a within-subjects or repeated measures design, where the same participants are measured under different conditions. Because each person serves as their own control, individual differences that would inflate variability in a between-subjects design get accounted for. A repeated measures ANOVA captures the correlation within and between groups over time, which lets it detect differences that a standard between-groups comparison might miss with the same number of participants.
One-Tailed Tests Concentrate Your Alpha
A two-tailed test at the 0.05 level splits your alpha between both directions: 0.025 in each tail. A one-tailed test puts the entire 0.05 in the direction you predict, giving you more power to detect an effect in that specific direction.
This sounds like a free lunch, but it comes with a real cost. A one-tailed test completely ignores the possibility of an effect in the opposite direction. If you’re testing whether a new drug is less effective than an existing one (and you genuinely don’t care if it’s more effective), a one-tailed test is appropriate. If there’s any chance that an opposite-direction effect matters, whether clinically, ethically, or scientifically, stick with two-tailed. Choosing a one-tailed test solely to reach significance is considered inappropriate practice.
Parametric Tests Are More Powerful Than Non-Parametric
When your data meet the assumptions of parametric tests (roughly normal distribution, equal variances), using a parametric test like a t-test or ANOVA gives you more power than its non-parametric equivalent. Non-parametric tests work by converting data to ranks, which discards information about the actual distances between values. That loss of information reduces power, sometimes dramatically.
This doesn’t mean you should force parametric tests on data that violate their assumptions. But when you have a choice, and your data reasonably satisfy the requirements, parametric tests will give you a better chance of detecting a real effect.
Why More Power Isn’t Always Better
Power is generally something you want more of, but there is a ceiling on its usefulness. Overpowered studies, particularly large pharmaceutical trials with 500 or more patients per group, can detect differences so tiny they have no practical or clinical meaning. A blood pressure drug that lowers readings by 0.5 mmHg might reach statistical significance with a big enough sample, but that difference is meaningless for a patient’s health.
This is why effect size and power should always be considered together. The goal isn’t just to get a p-value below 0.05. It’s to detect effects that are large enough to matter. When planning a study, the best approach is to decide in advance what the smallest meaningful effect would be, then calculate the sample size needed to detect that specific effect at 80% power and your chosen alpha level. That keeps the focus where it belongs: on results that are both statistically and practically significant.