Power Analysis Calculator

Before launching a study, whether clinical, academic, or commercial, you need to know how many subjects are required, and that's where our power analysis calculator comes in. You won't find another tool that's as easy to use for quickly estimating the minimum number of subjects required to detect an effect with statistical confidence.

Continue reading to learn more about:

• What is power analysis in research;
• How to use our statistical power analysis calculator; and
• How to do power analysis calculations in genomics using an example.

Ready to power up your study design?

If you've ever wondered, "What is power analysis in research?", you're not alone. We were wondering the same thing ourselves, which is why we wrote this article and created the power analysis calculator. Put simply, it's a method for determining the number of participants in a study needed to detect a significant difference, assuming the effect exists.

A power analysis to calculate sample size depends on several key parameters, each of which plays a role in the sensitivity of your study to detect real effects:

• Baseline incidence or mean (for dichotomous or continuous outcomes): If you're studying an uncommon outcome, such as a rare side effect, you'll generally need a larger sample size to detect differences between groups. Likewise, if you're comparing means, knowing the expected baseline (e.g., average blood pressure) helps define the size of the change you hope to detect.

• Population variance (standard deviation): The greater the variability of the data, the more difficult it is to distinguish real effects from random noise. Therefore, the higher the variance (standard deviation), the more participants you'll need to detect a significant difference.

• Effect size (expected difference between groups): This is the size of the difference you expect or hope to observe, whether it's a 15% decrease in risk or a 10% improvement in test results. If the effect is small, a larger number of patients will be needed to detect a difference.

• Alpha (α, significance level): This represents the risk you're willing to take of making a type I error, i.e., finding a difference when there isn't one. Most studies use α = 0.05, which means that there is a 5% chance that a statistically significant result is due to chance and is not a true difference.

• Beta (β): This is the probability of committing a type II error, i.e., failing to detect a real difference when one exists. Beta is directly related to the power of the study (power = 1 - β). Most medical publications use a beta threshold of 20% (0.2), which gives a power of 80%, meaning that there is an 80% chance that your study will detect a real effect if it exists.

Want to walk through this process step by step? Check out the section below on how to do a power analysis with our statistical power analysis calculator.

The statistical power analysis calculator offers four different configurations, but don't worry, it's not as complicated as it sounds; simply follow the steps that suit your study:

1. First, choose your study group design:

   • Two independent groups (e.g., treatment vs. control); or
   • One group vs. population (e.g., test group vs. known mean).

2. Select your primary endpoint:

   • Dichotomous (yes/no): Used when measuring presence/absence (e.g., mortality, success); or
   • Continuous (mean): Used when measuring quantities (e.g., blood pressure, test scores).

3. Then, input these different values:

   • For dichotomous outcomes, enter anticipated incidence rates (%) in Group 1 and Group 2 fields;
   • For continuous outcomes, input means and standard deviations in Group 1 and Group 2 fields; and
   • Finally, use the dropdown menu to express the effect size as an absolute value (choose Incidence (absolute value) or Mean (absolute value)) or as a % increase/decrease.

   If you are unsure about a result's frequency or want to validate your assumptions, you can explore the data's probabilities using the normal probability for sampling distributions calculator.

4. Set the statistical parameters:

   • Enrollment ratio: The ratio of group 2 to group 1 enrollment (default value is 1);
   • Alpha (default value is α = 0.05); and
   • Power (default value is 1 - β = 80%).

5. That's it! A new section appears with the minimum sample size required for your study.

Knowing how to calculate the Z-score can provide more in-depth information for your study. So, don't hesitate to visit the Z-score calculator.

Imagine you're planning a genomic study to compare gene expression levels between two independent groups (e.g., patients with a specific disease vs. a healthy control group). You expect a particular gene variant to be expressed in 30% of the disease group, compared with 15% of the control group. All you need to do is use our power analysis calculator to determine the appropriate sample size. Use it as follows:

1. In the "Study power inputs" section, select:
   • Two independent study groups, because you're comparing two distinct populations; and
   • Dichotomous (yes/no): here, you're measuring whether a gene variant is expressed.
2. Then, input the value for your study:
   • Group 1 incidence: 30%;
   • Group 2 incidence: 15%; and
   • Choose Incidence (absolute value) from the "Effect size" dropdown menu.
3. Finally, define the statistical parameters. Here, we leave the typical values set as defaults:
   • Enrollment ratio: 1 (equal sample sizes in both groups);
   • Alpha: 0.05; and
   • Power: 80%.
4. The power analysis sample size calculator shows that to detect this difference in gene expression with statistical confidence, you would need around 120 participants per group (240 in total).

Whether comparing means, proportions, or testing a group against a known population, our power analysis and sample size calculator helps you easily determine the minimum number of subjects needed to obtain reliable results. Together with other tools such as our margin of error calculator, you'll be perfectly equipped to plan rigorous and efficient research.