If you work with experiments, you eventually hit the same wall: “How many subjects do I actually need?” That’s where power analysis steps in, and it gets especially interesting when you’re comparing more than two groups with ANOVA. In this guide, we walk through practical, real-world examples of power analysis for ANOVA examples that researchers actually face in 2024 and 2025. Instead of abstract formulas, we’ll talk about a clinical trial comparing three blood pressure drugs, a marketing test of four ad designs, an education study of teaching methods, and several other cases where ANOVA is the right tool. For each example of power analysis for ANOVA, we’ll spell out the question, the design, the effect size assumptions, and how you’d use software like G*Power, R, or SAS to get your sample size. Along the way, you’ll see how small decisions about variance, effect size, and significance level can dramatically change the required number of participants.
If you’ve ever stared at a sample size calculator and thought, “This can’t possibly handle my messy real-world study,” you’re exactly the audience for this guide. In modern research, the best examples of simulation for power analysis come from situations where textbook formulas fail: clustered data, nonlinear effects, adaptive designs, missing data, and all the other things that make reviewers nervous. In this article, we’ll walk through **examples of simulation for power analysis: 3 practical examples** that mirror how statisticians actually work in 2024–2025. We’ll start with a clinical trial, move to an education study with clustered schools, and finish with a logistic regression scenario where the outcome is rare. Along the way, we’ll add several extra mini-scenarios so you can see how flexible simulation can be. You’ll see code-style logic (R or Python friendly), realistic parameter choices, and links to external resources from places like the NIH and major universities. By the end, you should feel confident designing your own simulation-based power analysis instead of forcing your study into the wrong formula.
If you run studies or A/B tests, you’ve probably heard that you need an effect size before you can run a power analysis. That’s where many people stall. They know they need it, but not how to get it in practice. This guide walks through real, practical examples of effect size calculations for power analysis so you can stop guessing and start planning studies with numbers that make sense. We’ll move beyond textbook definitions and walk through real examples from clinical trials, psychology experiments, education research, and online product testing. Along the way, you’ll see how to convert familiar quantities—mean differences, standard deviations, proportions, correlations, odds ratios—into effect sizes that software like G*Power, R, or Python can use. By the end, you’ll have a toolkit of examples of effect size calculations for power analysis that you can adapt directly to your own work, whether you’re in academia, industry, or public health.
If you’re trying to learn power analysis, staring at the G*Power interface can feel intimidating until you see concrete, real-world examples of G*Power power analysis examples in action. Instead of abstract theory, this guide walks through realistic study scenarios and shows how researchers actually use G*Power to pick sample sizes, check power, and justify their methods in papers and grant proposals. We’ll move through multiple example of G*Power power analysis scenarios: a clinical trial comparing two treatments, a psychology experiment with repeated measures, an education study using ANOVA, and a regression example from public health. These are the kinds of real examples you see in published work and institutional review board (IRB) applications. Along the way, I’ll point out how to choose effect sizes, set alpha, interpret power, and avoid the most common mistakes. By the end, you’ll have a concrete toolkit of examples of G*Power power analysis examples you can adapt to your own research, whether you’re in health sciences, psychology, education, or social science.
If you run experiments or analyze data and care about not wasting time, you need real, practical examples of statistical power analysis for t-tests. Power analysis tells you how many observations you need to reliably detect an effect of a given size. Without it, you’re either underpowered (high risk of missing real effects) or wildly over-sampling (burning budget and participant goodwill). In this guide, we walk through multiple examples of statistical power analysis for t-tests in everyday research: clinical trials, A/B tests, education studies, and more. You’ll see how to choose effect sizes, how alpha and power interact, and how paired versus independent t-tests change your sample size. These examples include both hand-calculation logic and software-based workflows using tools like G*Power and R. Whether you’re planning your first experiment or tightening up a mature research pipeline, these real examples of power analysis will help you design smarter, more efficient t-test studies.
If you run experiments with both between‑subjects and within‑subjects factors, you eventually hit the same wall: how many participants do I actually need? That’s where examples of power analysis for mixed-design ANOVA become very handy. Instead of staring at G*Power wondering what to click, it helps to see real examples, with real numbers, and real decisions. In this guide, I walk through multiple examples of power analysis for mixed-design ANOVA examples drawn from psychology, education, medicine, and user‑experience research. You’ll see how researchers specify effect sizes, choose alpha levels, deal with correlations among repeated measures, and translate all of that into sample size targets. Along the way, I’ll point to current practices and recommendations from 2024–2025 papers and methods notes, so the advice isn’t frozen in a 2010 G*Power screenshot. If you’ve ever thought, “I just want a concrete example of how to plan a mixed-design ANOVA study,” this is written for you.