Master Statistical Power Analysis with AI
Calculate sample sizes, effect sizes, and statistical power for any study design. From t-tests to complex ANOVA models, get accurate power calculations in seconds.
Statistical power analysis is the cornerstone of robust research design. Whether you're planning a clinical trial, designing an A/B test, or conducting academic research, understanding power helps you make informed decisions about sample sizes and detect meaningful effects.
But here's the thing—traditional power analysis often involves complex formulas, multiple software packages, and hours of calculations. What if you could perform comprehensive power analysis using natural language, get instant results, and explore different scenarios with AI assistance?
The Four Pillars of Power Analysis
Every power analysis revolves around four interconnected elements. Master these, and you'll never struggle with sample size calculations again.
Statistical Power (1-β)
The probability of detecting an effect when it truly exists. Typically set at 0.80 or 0.90, representing your study's sensitivity to real differences.
Effect Size (d or η²)
The magnitude of the difference you want to detect. Small effects need larger samples, while large effects can be detected with fewer observations.
Sample Size (n)
The number of participants or observations needed. This is often what you're solving for, but can also be a constraint in your analysis.
Alpha Level (α)
Your significance threshold, typically 0.05. This represents the probability of falsely rejecting the null hypothesis (Type I error).
Real-World Power Analysis Examples
Example 1: Clinical Trial Sample Size
A pharmaceutical company wants to test whether a new blood pressure medication is more effective than the current standard. They need to detect a 5 mmHg difference in systolic blood pressure reduction.
Using historical data, they know the standard deviation is approximately 15 mmHg. With α = 0.05, power = 0.80, and a two-tailed test, they need 143 participants per group (286 total).
Example 2: A/B Test for Conversion Rate
An e-commerce platform currently has a 3% conversion rate and wants to detect a 20% relative improvement (from 3% to 3.6%). For a power of 0.80 and α = 0.05, they need approximately 8,800 visitors per variant.
Example 3: Academic Research - ANOVA Design
A psychology researcher wants to compare four different therapy approaches. They expect a medium effect size (f = 0.25) and want 90% power. The analysis shows they need 45 participants per group (180 total).
How to Perform Power Analysis in Sourcetable
Follow this systematic approach to get accurate power calculations for any statistical test.
Define Your Research Question
Start by clearly stating what you want to test. Are you comparing means, proportions, or testing correlations? The test type determines your approach.
Specify Your Parameters
Input your desired power level (usually 0.80), significance level (typically 0.05), and expected effect size based on literature or pilot data.
Calculate Sample Size
Use Sourcetable's AI to perform the calculations. Simply ask: 'Calculate sample size for a t-test with power 0.80, alpha 0.05, and effect size 0.5'
Explore Scenarios
Adjust parameters to see how changes affect your sample size requirements. What if you increase power to 0.90? What about a smaller effect size?
When You Need Power Analysis
Power analysis is essential across industries and research domains. Here's where it makes the biggest impact.
Clinical Trials & Medical Research
Determine sample sizes for drug efficacy studies, medical device trials, and epidemiological research. Ensure your study can detect clinically meaningful differences.
Product & Marketing Analytics
Design A/B tests for website optimization, email campaigns, and product features. Know how long to run tests and when you have enough data.
Academic & Social Research
Plan psychology experiments, education studies, and social science research. Calculate sample sizes for surveys, experimental designs, and observational studies.
Quality Control & Manufacturing
Design sampling plans for quality control, process improvement studies, and manufacturing experiments. Ensure you can detect defects or process changes.
Financial & Risk Analysis
Plan studies for investment strategies, risk model validation, and market research. Calculate sample sizes for backtesting and validation studies.
Survey Research & Polling
Determine sample sizes for public opinion polls, customer satisfaction surveys, and market research studies. Account for response rates and subgroup analysis.
Beyond Basic Power Analysis
Multiple Comparisons & Family-Wise Error Rate
When you're testing multiple hypotheses, your effective alpha level changes. The Bonferroni correction is conservative but simple: divide α by the number of comparisons. For more sophisticated approaches, consider the False Discovery Rate (FDR) method.
Unequal Group Sizes
Real-world studies often have unequal groups. The harmonic mean formula adjusts for this: n_harmonic = 2 × (n1 × n2) / (n1 + n2). This gives you the 'effective' sample size for power calculations.
Non-Normal Data & Non-Parametric Tests
When your data doesn't follow normal distributions, non-parametric tests like Mann-Whitney U or Kruskal-Wallis may be more appropriate. These typically require 15-20% larger sample sizes to achieve equivalent power.
Repeated Measures & Longitudinal Studies
Studies with repeated measurements benefit from reduced within-subject variability. The correlation between repeated measures can dramatically reduce required sample sizes—sometimes by 50% or more.
Avoiding Power Analysis Pitfalls
Mistake 1: Using Inappropriate Effect Sizes
Cohen's conventions (small = 0.2, medium = 0.5, large = 0.8) are guidelines, not rules. Always base effect sizes on previous research, pilot data, or clinical significance thresholds.
Mistake 2: Ignoring Practical Constraints
Your power analysis might suggest 500 participants, but you only have access to 100. Consider increasing your effect size threshold or using more sensitive measures rather than proceeding with underpowered studies.
Mistake 3: Post-Hoc Power Analysis
Calculating power after seeing your results is largely meaningless. If you didn't find significance, the post-hoc power will be low by definition. Focus on confidence intervals and effect size estimation instead.
Mistake 4: Forgetting About Attrition
Longitudinal studies and clinical trials often lose participants. Inflate your initial sample size by the expected dropout rate: n_adjusted = n_calculated / (1 - dropout_rate).
Frequently Asked Questions
What's the difference between statistical power and statistical significance?
Statistical significance (α) is the probability of a Type I error—falsely rejecting a true null hypothesis. Statistical power (1-β) is the probability of correctly rejecting a false null hypothesis. High power means you're likely to detect real effects; low significance thresholds mean you're unlikely to claim false effects.
How do I choose an appropriate effect size for my study?
Start with previous research in your field, pilot data, or the minimum clinically/practically meaningful difference. If none are available, use Cohen's conventions as a starting point, but always justify your choice. Small effects often require very large samples to detect reliably.
Is 80% power always sufficient?
Not necessarily. For exploratory research, 70-80% might be acceptable. For confirmatory studies or when Type II errors are costly (like missing a beneficial treatment), consider 90% or higher. The cost of additional participants should be weighed against the risk of missing true effects.
Can I perform power analysis for complex designs like mixed-effects models?
Yes, but it's more complex than simple t-tests. You need to consider the correlation structure, number of levels, and variance components. Simulation-based approaches are often more accurate than formula-based methods for complex designs.
What if my pilot study shows a different effect size than expected?
Recalculate your power analysis with the new effect size estimate. If the required sample size changes dramatically, consider whether your original expectations were realistic or if you need to adjust your study design.
How do I handle multiple primary endpoints in power analysis?
For multiple co-primary endpoints (all must be significant), use the Bonferroni correction or other multiple comparison adjustments. For multiple primary endpoints where any one being significant is sufficient, the power calculation is more complex and often requires simulation.
Sourcetable Frequently Asked Questions
How do I analyze data?
To analyze spreadsheet data, just upload a file and start asking questions. Sourcetable's AI can answer questions and do work for you. You can also take manual control, leveraging all the formulas and features you expect from Excel, Google Sheets or Python.
What data sources are supported?
We currently support a variety of data file formats including spreadsheets (.xls, .xlsx, .csv), tabular data (.tsv), JSON, and database data (MySQL, PostgreSQL, MongoDB). We also support application data, and most plain text data.
What data science tools are available?
Sourcetable's AI analyzes and cleans data without you having to write code. Use Python, SQL, NumPy, Pandas, SciPy, Scikit-learn, StatsModels, Matplotlib, Plotly, and Seaborn.
Can I analyze spreadsheets with multiple tabs?
Yes! Sourcetable's AI makes intelligent decisions on what spreadsheet data is being referred to in the chat. This is helpful for tasks like cross-tab VLOOKUPs. If you prefer more control, you can also refer to specific tabs by name.
Can I generate data visualizations?
Yes! It's very easy to generate clean-looking data visualizations using Sourcetable. Simply prompt the AI to create a chart or graph. All visualizations are downloadable and can be exported as interactive embeds.
What is the maximum file size?
Sourcetable supports files up to 10GB in size. Larger file limits are available upon request. For best AI performance on large datasets, make use of pivots and summaries.
Is this free?
Yes! Sourcetable's spreadsheet is free to use, just like Google Sheets. AI features have a daily usage limit. Users can upgrade to the pro plan for more credits.
Is there a discount for students, professors, or teachers?
Currently, Sourcetable is free for students and faculty, courtesy of free credits from OpenAI and Anthropic. Once those are exhausted, we will skip to a 50% discount plan.
Is Sourcetable programmable?
Yes. Regular spreadsheet users have full A1 formula-style referencing at their disposal. Advanced users can make use of Sourcetable's SQL editor and GUI, or ask our AI to write code for you.
Ready to Master Statistical Power Analysis?
Stop second-guessing your sample sizes. Get precise power calculations and design robust studies with AI-powered statistical analysis tools.