# Using the Humblytics A/B Sample‑Size Calculator

A/B (split) testing compares a **control (Variant A)** with a **variation (Variant B)** so you can make evidence‑based improvements to your website or app. The Humblytics Sample‑Size Calculator tells you *exactly* how many visitors each variant needs before you can trust the result.

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### 1. Why Sample Size Matters

| Too Small                                       | Just Right                                     | Too Large                                         |
| ----------------------------------------------- | ---------------------------------------------- | ------------------------------------------------- |
| Results look erratic; you risk acting on noise. | Detects real differences with high confidence. | Wastes time and traffic without adding precision. |

Choosing the correct sample size balances statistical rigour with business velocity.

***

### 2. Input Definitions

| Field                               | What It Means                                                | Example                                 |
| ----------------------------------- | ------------------------------------------------------------ | --------------------------------------- |
| **Baseline Conversion Rate**        | Your *current* conversion rate.                              | `5 %` (5 of every 100 visitors convert) |
| **Minimum Detectable Effect (MDE)** | The smallest lift you care about.                            | `+1 %` absolute (from 5 % → 6 %)        |
| **Statistical Significance**        | Confidence level that the observed lift is real, not random. | `95 %` (industry default)               |
| **Statistical Power**               | Probability of detecting an effect *if* it exists.           | `80 %` (common default)                 |

> **Tip:** Lower MDE or higher confidence / power settings will increase the required sample size.

***

### 3. Step‑by‑Step

1. **Open** the Humblytics Sample‑Size Calculator.
2. **Enter** each value defined above.
3. Click **Calculate**.
4. **Record** the required visitors per variant shown in the results panel.
5. **Plan** your test window so you can realistically hit those numbers.

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### 4. Worked Example

* **Baseline Conversion Rate:** `5`
* **MDE:** `1`
* **Significance:** `95`
* **Power:** `80`

▶︎ **Result:** *≈ 4,000 visitors* per variant (total ≈ 8,000 sessions). Run the experiment until both A and B have reached these counts before analysing.

***

### 5. Best‑Practice Reminders

* **One Variable at a Time** — isolate the element you’re testing.
* **Run Full Business Cycles** — capture weekday/weekend traffic differences.
* **Don’t Peek Early** — premature stops inflate false‑positive risk.
* **Look Beyond Win/Loss** — examine bounce rate, engagement, revenue per visitor.
* **Iterate** — document learnings and queue up the next hypothesis.

Following this workflow ensures every Humblytics test is powered correctly, statistically sound, and focused on meaningful business impact.


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