Statistically rigorous A/B testing.
From experiment design to confident decisions.

Plan valid experiments, assess feasibility before you ship, and interpret results with statistical confidence — all in one experimentation platform.

Built for data-driven teams · Grounded in first-principles statistics · Privacy-first by design

A complete experimentation workflow

1

Design experiments

🧩

Define metrics, hypotheses, traffic splits, and assumptions.

2

Validate feasibility

📐

Understand sample size, MDE, and detectability.

3

Analyze & decide

📊

Compare variants and get statistically sound guidance.

What is A/B testing?

Causal inference · Randomized experiments

A/B testing is a causal inference method that measures the true impact of a change by randomly assigning users to variants.

Unlike observational analytics, A/B tests isolate cause and effect — allowing teams to make decisions under uncertainty with quantified risk.

  • Randomization controls for confounders
  • Sample size determines decision reliability
  • Inference separates signal from noise
Learn how to design statistically valid experiments →
1
Change introduced

Variant A vs Variant B

2
Users randomized

Bias and selection effects removed

3
Outcomes compared

Statistical uncertainty quantified

Why most A/B tests produce misleading results

False confidence from small samples

Teams stop experiments early because results appear statistically significant.

In reality, small samples inflate variance and make random noise look like signal — leading to false wins and costly rollouts.

Validate required sample size before launch →

Experiments underpowered by design

Many teams plan tests assuming unrealistic effect sizes.

If your minimum detectable effect cannot be observed with available traffic, the experiment cannot succeed — no matter how long it runs.

Check detectability and MDE upfront →

Metrics chosen after results appear

Success criteria are often redefined once data is visible.

This breaks statistical validity, introduces bias, and turns experimentation into post-hoc storytelling instead of inference.

Lock hypotheses and metrics before testing →

Experimentation tools

📋
Design Validator
🔢
Sample Size
🎯
Detectable Effect (MDE)
📊
Result Analysis

A/B testing for every team

Product Managers

  • Validate hypotheses
  • Avoid false wins
  • Decide responsibly

Marketers

  • Avoid false positives
  • Plan around traffic
  • Measure real lift

Data Teams

  • Enforce rigor
  • Avoid p-hacking
  • Produce auditable results

About the platform

Platform philosophy

pvalue.net is built as a data science–driven platform grounded in first-principles statistics, designed to help teams make correct decisions under uncertainty.

Modern analytics tools optimize for speed and presentation. pvalue.net optimizes for decision correctness.

Every workflow is designed to reduce false confidence, surface uncertainty early, and force explicit decision commitment.

Built on statistical rigor

Statistics is not a feature in pvalue.net — it is the foundation.

  • First-principles statistical modeling
  • No hidden defaults or silent assumptions
  • Decision-oriented outputs, not vanity metrics

Long-term vision

pvalue.net is being built as a data science platform for business decision systems.

Today, the focus is experimentation — because experimentation exposes the cost of incorrect decisions most clearly.

Over time, the platform will expand to support multiple data-driven workflows grounded in statistical efficiency, transparency, and accountability.

Data-driven decision systems visualization

Pricing

Currently FREE

Full access to all experimentation tools

pvalue.net is free to use while we focus on building a statistically rigorous experimentation platform teams can trust.

WHAT'S AVAILABLE TODAY ?

  • Design validation & feasibility checks
  • Sample size & MDE calculators
  • Statistical result analysis
  • Decision guidance grounded in inference

PLANNED PAID CAPABILITIES :

  • Experiment history & decision logs
  • Team collaboration & governance
  • Advanced validation & audit trails
  • Organization-level experimentation analytics

Free access does not mean reduced rigor or limited functionality.

Stop guessing. Move to statistically sound decisions, Now!