Sample Size Calculator Optimizely

Determine the traffic required for statistically significant A/B test results.

Total Traffic Required
31,000

Absolute MDE
1.00%

Z-Alpha (2-tail)
1.96

Z-Beta
0.84

Estimated Sample Size Requirements for Various MDEs

Relative MDE (%)	Absolute Conversion Goal	Visitors Per Variation	Total Visitors (A/B)

What is a Sample Size Calculator Optimizely?

A sample size calculator optimizely is a specialized statistical tool designed to help conversion rate optimization (CRO) specialists and digital marketers determine the volume of traffic necessary to reach a valid conclusion in an A/B test. Unlike a simple calculator, the sample size calculator optimizely accounts for the specific nuances of digital experimentation, including baseline conversion rates and the minimum detectable effect (MDE).

Who should use it? Anyone running experiments—from product managers to data scientists—must use a sample size calculator optimizely to ensure their tests are “powered” correctly. A common misconception is that you can simply stop a test when it looks “significant.” However, without calculating the required sample size beforehand, you risk “peaking” at the data, which leads to false positives (Type I errors).

Sample Size Calculator Optimizely Formula and Mathematical Explanation

The underlying math of the sample size calculator optimizely relies on power analysis for the difference between two proportions. The step-by-step derivation involves finding the point where the probability of a Type I error (alpha) and Type II error (beta) are balanced.

The standard formula used for determining visitors per variation is:

n = [ (Z_α/2 + Z_β)² * 2 * p * (1 – p) ] / d²

Variable	Meaning	Unit	Typical Range
n	Sample Size Per Variation	Count	100 – 1,000,000+
p	Baseline Conversion Rate	Decimal	0.01 – 0.50
d	Absolute MDE	Decimal	0.001 – 0.10
Zα/2	Critical Value for Significance	Constant	1.96 (for 95%)
Zβ	Critical Value for Power	Constant	0.84 (for 80%)

Practical Examples (Real-World Use Cases)

Example 1: E-commerce Checkout Optimization

An e-commerce manager wants to test a new “Buy Now” button color. The current conversion rate is 5% (Baseline). They want to detect at least a 10% relative improvement (MDE). Using the sample size calculator optimizely with 95% significance and 80% power, the calculation shows they need approximately 30,000 visitors per variation to ensure the results are reliable.

Example 2: SaaS Landing Page Header

A SaaS company has a high-performing landing page with a 20% conversion rate. They are testing a radical new headline but only have enough traffic to detect a 5% relative change. The sample size calculator optimizely indicates a requirement of roughly 12,000 visitors per variation. If they only had 2,000 visitors, the tool would warn them that their experiment is underpowered.

How to Use This Sample Size Calculator Optimizely

1. Enter Baseline Conversion Rate: Look at your historical analytics for the page you are testing and enter that percentage.
2. Define MDE: Decide how much of a lift is “meaningful” for your business. Smaller lifts require significantly more traffic.
3. Select Significance: 95% is the industry standard. This means you are 95% sure the result isn’t a fluke.
4. Select Power: 80% is common, meaning you have an 80% chance of detecting the lift if it actually exists.
5. Review Results: The sample size calculator optimizely will instantly show the visitors needed per variation.

Key Factors That Affect Sample Size Calculator Optimizely Results

• Baseline Conversion Rate: Higher baseline rates generally require smaller sample sizes because the variance is easier to manage until you hit very high percentages.
• Minimum Detectable Effect (MDE): This is the most sensitive factor. Halving your MDE (e.g., from 10% to 5%) quadruples the required sample size in a sample size calculator optimizely.
• Statistical Significance: Aiming for 99% significance instead of 95% increases the required traffic to reduce the risk of false positives.
• Statistical Power: Increasing power (e.g., to 90%) reduces the risk of “missing” a winning variation (false negatives) but requires more data.
• Traffic Volume: Your actual daily traffic limits how long the test must run. A sample size calculator optimizely tells you the total, but your calendar determines the duration.
• Variation Count: If you are running an A/B/C test, you must multiply the “per variation” result by the total number of variations (3 in this case).

Frequently Asked Questions (FAQ)

1. Why is the sample size calculator optimizely result so high?

Usually, this happens because the MDE is set too low. Detecting a 1% lift is mathematically much harder than detecting a 10% lift, requiring significantly more data points.

2. Can I stop my test early if I see a “winning” result?

No. Stopping early based on a “significant” P-value before reaching the sample size calculator optimizely target is called “data peaking” and leads to unreliable conclusions.

3. What happens if I don’t reach the required sample size?

Your test will be “underpowered.” This means you might conclude there is no difference between A and B when there actually is one (a Type II error).

4. How does baseline conversion affect the result?

The closer a conversion rate is to 50%, the more “variance” it has. However, in the sample size calculator optimizely, very low conversion rates (like 0.5%) also require huge samples to prove a relative lift.

5. What is the difference between relative and absolute MDE?

Relative MDE is a percentage of the baseline (10% lift on a 10% baseline = 11%). Absolute MDE is a percentage point increase (2% increase on a 10% baseline = 12%). Most use relative MDE.

6. Is 95% significance always necessary?

While 95% is standard for a sample size calculator optimizely, some low-risk internal tests may use 90% to move faster, while high-risk medical or financial changes might require 99%.

7. Should I include “bounced” visitors in my baseline?

It is best to only include visitors who actually had the “opportunity” to convert. Excluding bounces can make your baseline conversion rate more accurate for the sample size calculator optimizely.

8. Does this calculator work for multivariate tests (MVT)?

Yes, but you must calculate the sample size for the specific “cell” or variation you are comparing, and realize that MVT often requires massive amounts of traffic.