Free alternatives to statistical-power
50 free AI Skills apps to use instead, best rated first.
Top picks
- W
webapp-testing
LaunchNative Python Playwright scripts for testing local web applications with server lifecycle management. Includes with_server.py helper to automatically start and manage single or multiple servers before running automation scripts. Supports re
- C
content-research-writer
LaunchAssists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on
- O
openhue
Launchopenhue is an agent skill from the OpenClaw project, listed on skills.sh. It lets an agent control Philips Hue lights and scenes through a Hue Bridge with the OpenHue CLI: turn lights on and off, dim rooms, set scenes such as movie mode, an
- B
batch-grill-me
Launchbatch-grill-me is an agent skill from Matt Pocock's skills collection, listed on skills.sh. It interviews you relentlessly about a plan until you share an understanding. It maps decisions as a tree and asks every question it can answer now
- I
implement
Launchimplement is an agent skill from Matt Pocock's skills collection, listed on skills.sh. It implements work from a spec or tickets: it fetches the ticket, works test-first at agreed seams, runs type checks and tests as it goes, reviews the re
- F
frontend-design
LaunchDistinctive, production-grade frontend interfaces that reject generic AI aesthetics through intentional design choices. Guides aesthetic direction selection (brutalist, maximalist, retro-futuristic, luxury, organic, etc.) before implementat
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brainstorming
Transform rough ideas into fully-formed designs through structured questioning
finishing-a-development-branch
Guides completion of development work by presenting clear options
test-driven-development
Use when implementing any feature or bugfix, before writing implementation code
About statistical-power
statistical-power
Calculates sample sizes and statistical power for study planning
statistical-power is an agent skill for planning studies by calculating sample sizes and statistical power. It answers how many independent experimental units are needed to detect an effect of scientific importance, or what effects a feasible sample could detect. The skill says to choose the inferential goal first, because precision, equivalence, non-inferiority and sequential monitoring each need their own calculations that the bundled superiority-test helpers do not cover. For any given test, four quantities are linked: sample size, effect size, significance level and power. Fixing three of them lets you solve for the fourth, when a solution exists, for a fixed design, analysis and nuisance parameters. The skill covers analytical calculations, which are exact under standard assumptions and approximate otherwise, and simulation or Monte Carlo methods, which need a credible data-generating process and a calibrated planned analysis. It also helps you choose and convert effect sizes, which is often the hardest part. Use it when someone asks how many subjects, samples or replicates are needed, or wants an a priori power analysis.
- Price
- Free
- Made by
- k-dense-ai
- Category
- AI Skills
- Website
- github.com/k-dense-ai/scientific-agent-skills/statistical-power