Make the call.
Ship with confidence.
Turn messy data questions into defensible decisions in minutes — with the right test, proper effect sizes, and stakeholder-ready artifacts.
Confidence
95%
Intervals you can defend
Power
0.82
Sample sizes in minutes
Effect
+12.4%
Measured, not guessed
Sound Familiar?
The statistical uncertainty tax
Wrong test choices. Missing confidence intervals. Results that don't survive review. Decisions that get reversed.
"Is this actually significant?"
Staring at a p-value, unsure if it means anything real
"Can we really ship this?"
Second-guessing whether the test was even the right one
"What's the actual impact?"
Struggling to explain effect size to stakeholders
What Changes
From uncertainty to clarity
Every analysis becomes defensible. Every result becomes actionable.
"I think it's significant"
95% CI: [2.1%, 4.8%]
"We probably need more data"
n=2,400 achieves 80% power
"Hard to say what happened"
Effect size d=0.34 (small-med)
"Let me check with someone"
Here's the Python + writeup
In Three Steps
From question to decision
Describe your question
Answer a few quick questions about your data, goal, and constraints. No statistics PhD required.
Get the right test
StatsTest identifies the correct statistical method, checks assumptions, and flags edge cases.
Ship with confidence
Get effect sizes, confidence intervals, Python/R code, and a stakeholder-ready explanation.
Built for Applied Analysts
Everything you need to ship decisions
Experiment Design
Plan clean tests that survive scrutiny. Guard against bias, handle edge cases, and ship results you can defend.
Sample Size Calculator
Know exactly how many users you need before you start. Calculate power, detect minimum effects, and avoid underpowered tests.
Effect Size + CI
Quantify impact properly with effect sizes and confidence intervals. Report the numbers that actually matter for decisions.
Code Generation
Get Python and R code ready to paste into your notebook. No more Stack Overflow spelunking for the right syntax.
Stakeholder Artifacts
Generate clear writeups that explain what you did, what it means, and what could invalidate it. Get buy-in faster.
Assumption Checking
Catch violations before they catch you. Verify normality, homogeneity, and independence requirements automatically.
Trusted By
Teams that ship with confidence
StatsTest helped us standardize our experiment analysis across 50+ analysts. Fewer reversals, faster decisions, and results that actually hold up in review.
Staff Data Scientist
Series B Tech Company
From the Blog
Practical guides for better analysis
Statistical methods, experiment design, and decision tooling for modern teams.
Accelerated Failure Time Models: When Cox Doesn't Fit
When proportional hazards fail, AFT models offer an interpretable alternative. Learn when to use accelerated failure time models, how to interpret time ratios, and how they compare to Cox regression.
Autocorrelation: Why Your Daily Metrics Aren't Independent
Learn why autocorrelation in product metrics invalidates standard tests, how to detect it, and what corrections to apply.
Bayesian A/B Testing: Posterior Probabilities for Ship Decisions
How to run Bayesian A/B tests that give you the probability a variant wins. Practical guide with Python code for conversion rates and revenue metrics.
Ready to make better decisions?
Stop second-guessing your statistics. Get the right test, proper effect sizes, and artifacts that survive review.