Yes, increasing the alpha level (significance threshold) in a statistical test directly increases the power of that test. Power is the probability of correctly detecting a real effect, and a higher alpha makes it easier to reach that detection threshold. But this boost in power comes with a trade-off: a greater risk of a false positive result.
How Alpha and Power Are Connected
Statistical power depends on the significance level at which a test is performed. When you set alpha at 0.05, you’re saying you’ll accept a 5% chance of concluding there’s an effect when there isn’t one. If you raise alpha to 0.10, you widen the door for declaring a result “significant,” which means you’re more likely to catch a real effect, but also more likely to flag a false one.
The mechanics are straightforward. Your test produces a test statistic, and alpha determines the critical value that statistic must exceed to reject the null hypothesis. A larger alpha lowers that critical value, making it easier to reject the null. When the null hypothesis is actually false (meaning a real effect exists), this lower bar means you’ll reject it more often, which is exactly what power measures. When the null hypothesis is actually true, that same lower bar means you’ll incorrectly reject it more often, which is the false positive (Type I error) that alpha controls.
The Type I and Type II Error Trade-Off
Every statistical test juggles two kinds of mistakes. A Type I error is a false positive: concluding something works when it doesn’t. A Type II error is a false negative: missing a real effect. Alpha is the probability of a Type I error, and beta is the probability of a Type II error. Power equals 1 minus beta.
These two errors pull in opposite directions. Reducing the probability of one type of error increases the probability of the other, assuming everything else stays the same (sample size, effect size). So when you increase alpha and accept more false positive risk, beta decreases and power goes up. When you decrease alpha to be more cautious about false positives, beta increases and power drops. This is why researchers describe it as a “delicate balance” between the minimum allowed levels for both types of errors.
How Much Power Changes in Practice
The size of the power boost depends on your sample size, the true effect size, and how much you shift alpha. As a general pattern, moving alpha from 0.01 to 0.05 produces a noticeable jump in power, while moving from 0.05 to 0.10 adds a smaller increment. The gains are most dramatic when your study is borderline underpowered, meaning when it’s close to the 80% power threshold that most researchers target.
This relationship also works in reverse when thinking about sample size. If you lower alpha (making your test stricter), you need a larger sample to maintain the same power. If you raise alpha, you can achieve the same power with fewer participants. This is one reason pilot studies, which tend to be small, often use alpha levels of 0.10 or even 0.20. The relaxed threshold compensates for the limited sample and helps researchers detect signals worth investigating in a larger follow-up study.
Why Not Just Raise Alpha?
If a higher alpha gives you more power, you might wonder why researchers don’t just set alpha at 0.20 and call it a day. The reason is that false positives carry real costs. A drug approved based on a false positive exposes patients to side effects without any benefit. A policy enacted based on a false finding wastes public resources. The choice of alpha reflects a judgment about how much false positive risk is tolerable in a given context.
In most social science research, alpha is set at 0.05 by convention. The FDA requires clinical trials to control their overall Type I error probability at 2.5% (one-sided), which corresponds to a stricter 0.025 threshold, because the consequences of approving an ineffective drug are serious. At the other end of the spectrum, a 2017 proposal by a group of researchers led by Daniel Benjamin argued that psychology should lower its standard alpha from 0.05 to 0.005 to combat the replication crisis, on the grounds that findings with p-values near 0.05 represent weak evidence. That change would significantly reduce power unless researchers compensated with larger samples.
The optimal alpha for any given study depends on the relative costs of the two error types. When the prevalence of true effects is low and false positives are expensive, very small alpha values are optimal. When real effects are common and missing them is the bigger concern, larger alpha values make more sense. There’s no universal right answer.
Other Ways to Increase Power
Raising alpha is rarely the first or best strategy for boosting power, because it directly increases false positive risk. Researchers more commonly turn to alternatives that improve power without that trade-off.
- Increase sample size. Collecting more data is the most straightforward path to higher power. Larger samples produce more precise estimates, making it easier to detect real effects at any alpha level.
- Target a larger effect size. If you can design your intervention to produce a bigger difference between groups, power increases. This isn’t always controllable, but choosing a comparison condition wisely can help.
- Reduce measurement noise. Using more reliable instruments, standardizing procedures, or selecting a more homogeneous study population all reduce variability in your data, which increases power.
- Use a one-tailed test. If you have a strong directional hypothesis, a one-tailed test concentrates all of alpha in one direction, effectively giving you more power to detect effects in that direction. This is appropriate only when effects in the opposite direction are irrelevant or impossible.
Each of these strategies increases power independently. In practice, researchers combine them during the study design phase, running power analyses to find the combination of sample size, alpha, and expected effect size that achieves at least 80% power while keeping the false positive rate at an acceptable level.
Choosing the Right Alpha for Your Study
The standard 0.05 threshold works as a reasonable default for most research, but it isn’t sacred. The right alpha depends on what’s at stake. For exploratory or pilot work where the goal is generating hypotheses rather than confirming them, alpha levels of 0.10 or 0.20 are common and defensible. The cost of a false positive in this context is low because findings will be tested again in a larger study.
For confirmatory research, especially in medicine or public policy, stricter thresholds are appropriate. Studies where falsely concluding a treatment works could cause harm often use alpha values of 0.01 or 0.001. The lost power is compensated by enrolling more participants. In fields grappling with replication problems, the push toward 0.005 reflects a growing consensus that the traditional 0.05 threshold lets too many weak findings through.
Whatever alpha you choose, be transparent about the trade-off. A higher alpha gives you more power to detect real effects but increases the chance of chasing false leads. A lower alpha protects against false positives but raises the risk of missing something real. The goal is to match your error tolerance to the consequences of each type of mistake in your specific research context.