> For the complete documentation index, see [llms.txt](https://support.fibr.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://support.fibr.ai/hypothesis-and-experimentation/overview/key-concepts.md).

# Key Concepts

Before running experiments, understand these core concepts.

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#### Hypothesis

A hypothesis is a specific, testable prediction about what change will improve a metric.

**Good hypothesis:** "Changing the CTA from 'Learn More' to 'Get Started Free' will increase button clicks by 15% because it reduces perceived commitment."

**Bad hypothesis:** "Let's try a different button."

A strong hypothesis includes:

* What you're changing
* What metric will improve
* Why you believe this will work

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#### Variants

Variants are different versions of your page that you test against each other.

* **Control:** Your original page (unchanged)
* **Variant A, B, C...:** Modified versions

In an A/B test, you have one control and one variant. In a multivariate test, you test multiple variants simultaneously.

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#### Statistical Significance

Statistical significance tells you whether your results are real or just random chance.

Fibr calculates this automatically. When an experiment reaches statistical significance (typically 95% confidence), you can trust that the winning variant actually performs better.

**Before significance:** Results might flip. Don't make decisions yet.

**After significance:** Results are reliable. Roll out the winner.

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#### Traffic Allocation

Traffic allocation determines what percentage of visitors see each variant.

**50/50 split:** Half see control, half see variant. Fastest results but higher risk.

**90/10 split:** Most visitors see control, small group sees variant. Safer but slower.

Start with 50/50 unless you're testing something risky.