The Instant Sample Size Calculator for Researchers & Marketers
Stop guessing. Start validating. Get the precise sample size you need for your A/B tests, clinical trials, and research studies in seconds. 100% free and easy to use.
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Watch this demo to see how you can get your sample size in seconds.
📊 For Comparing Means
Perfect for when you are measuring continuous outcomes like revenue, blood pressure, or user engagement scores. Based on the powerful two-sample t-test.
📈 For Comparing Proportions
Ideal for A/B testing conversion rates, click-through rates, or comparing event rates in clinical trials. Based on the robust two-proportion z-test.
Why Sample Size Matters
An underpowered study is a waste of time and money. An overpowered study is inefficient. Finding the "Goldilocks" sample size is the foundation of credible research. It ensures your findings have sufficient statistical power to be meaningful, valid, and publishable. Whether you're a scientist seeking funding, a marketer optimizing a landing page, or a student writing a thesis, our tool gives you the confidence to design a robust study.
Frequently Asked Questions
What is statistical power?
Statistical power is the probability that a study will correctly detect an effect when there is a true effect to be found. A study with high power (typically 80% or more) is less likely to make a Type II error (a false negative). Our calculator helps you determine the sample size needed to achieve your desired power.
Why is sample size important for A/B testing?
In A/B testing, an adequate sample size is crucial to ensure that the observed difference between version A and version B is statistically significant and not just due to random chance. A small sample size can lead to inconclusive or misleading results, wasting time and resources.
What is an alpha level (significance level)?
The alpha level, or significance level (often denoted as α), is the probability of making a Type I error – concluding there is an effect when one doesn't actually exist (a false positive). The most common alpha level used in research is 0.05, which corresponds to a 5% chance of a false positive.