# A/B Testing Software: Convert Experiences is a privacy\-first A/B testing and experimentation platform for mid\-market e\-commerce brands and CRO agencies\. Features include client\-side and server\-side testing, feature flags, and 90\+ integrations\.

> Convert\.com’s A/B testing tool has split testing, personalization, advanced goals, fast support, and monthly payments\.

Generated by Yoast SEO v28.2, this is an llms.txt file, meant for consumption by LLMs.

## Pages
- [Robert Rubenko](https://www.convert.com/free-trial/robert-rubenko/)
- [Robert Rubenko](https://www.convert.com/certified-partners/robert-rubenko/)
- [WIE Summit 2026](https://www.convert.com/wie-summit-2026/)
- [Trusted Performance](https://www.convert.com/trusted-performance/)
- [Convert Intelligence Privacy \(Beta\)](https://www.convert.com/convert-intelligence-privacy/)

## Articles
- [A/B Testing Statistics: Why Statistics Matter in Experimentation](https://www.convert.com/blog/a-b-testing/decode-master-ab-testing-statistics/)
- [What Is A/B Testing? The Complete 2026 Guide](https://www.convert.com/blog/a-b-testing/ab-testing-guide/): A/B Testing is an experiment that compares two or more versions of a specific marketing asset to determine the best performer\. 
- [Optimizely Alternatives: Top A/B Testing Platforms to Consider for 2026](https://www.convert.com/blog/optimization-tools/optimizely-alternatives-for-ab-testing/)
- [How to Optimize Your Shopify Marketing Across the Board? \(With Real Examples From Growth and CRO Experts\)](https://www.convert.com/blog/shopify-ab-testing/optimize-shopify-marketing-with-ab-testing/)
- [Top 10 A/B Testing Tools That Are Good for the Next 5 Years \(Vetted by Features, Privacy, Maturity \& Price\)](https://www.convert.com/blog/a-b-testing/top-ab-testing-tools-2022/)

## Glossary
- [Peeking](https://www.convert.com/glossary/peeking/): Peeking refers to checking the interim results of an A/B test with the intent to take action before it completes\. It is very common for experiments to look at "significant" in the beginning due to noise in the data, novelty effects, etc\. This can lead to wrong decisions based on a subset of the sample that can be very costly for the organization\.

Peeking can be avoided by building a robust "test plan" where the sample size, significance level, and test duration are pre\-determined before starting an experiment\. It also helps to invest in educating the broader stakeholder group on why it’s important to wait until the test sample is reached before any decision is made\. Another less common alternative is using sequential tests \(instead of more commonly used "fixed sample" tests\), which allows for peeking but at the cost of sacrificing some statistical power\.
- [Sample Size](https://www.convert.com/glossary/sample-size/): I can't stress this enough, sample size is super important in A/B testing\. To make the best decisions, you must ensure that each test variation has a large enough sample size—that is, enough users or observations to collect solid statistical data for your analysis\.

Why does this matter? Well, think about it: if you only ask two friends for restaurant recommendations and assume their favorite is the best choice in town, you might miss out on some amazing places\. The same is true in testing\. You can't say which variation won with a small sample size\. This uncertainty can lead to decisions that might not be in your best interest in the long run\. So, having a good sample size isn't just a technical detail; it's key to making informed choices that can significantly impact your business\.
- [A/A Testing](https://www.convert.com/glossary/a-a-testing/): A/A tests aren’t a one\-time task—they should be a regular part of your experimentation process\. They help ensure your setup works as intended by verifying that traffic is correctly split \(e\.g\., 50/50\), all intended visitors are included, and key performance indicators \(KPIs\) are tracked properly\.

If an A/A test shows a big difference between the two identical versions, it could mean there’s a problem—like tracking errors, incorrect visitor splits, or other setup issues\. However, if the test results look normal \(inconclusive\), that doesn’t automatically mean everything is perfect\. And if you do see a difference, don’t panic\! Random chance can sometimes create false positives, depending on the significance level\. Instead of focusing on a single result, think of A/A testing as a routine health check for your platform\.

By incorporating A/A tests into your process, you can catch issues early and ensure your A/B test results lead to the right decisions\.
- [Sequential Testing in A/B Experiments: How It Works and When to Use It](https://www.convert.com/glossary/sequential-testing/): Sequential testing is useful for optimizing time\-sensitive assets or when real\-time decision\-making is critical\.

Sequential testing offers a robust framework for building automated decision\-making systems\. In digital advertising, for example, sequential testing can be used to test variations of time\-sensitive promotions or event\-based campaigns\. It allows teams to quickly identify the best\-performing variations and optimize their spending by showing them to everyone\.

Other common use cases include ephemeral content, ramps, fraud \& failure detection\. Sequential experiments are an extremely powerful tool in an experimenter's armory\.
- [Confidence Level](https://www.convert.com/glossary/confidence-level/): The higher the confidence level, the more confident you can be that your results are trustworthy\. In general, experimenters should aim to achieve a confidence level of 95% or higher\.

However, it's important to note the highest level you can get is 99\.99%\+\. You can never be absolutely 100% sure your data is entirely accurate\.

A 95% confidence level means the difference between versions is real and not just statistical noise or due to random chance\.

However, it's important to realize this metric does NOT mean there's a 95% chance of making the right decision based on the test results\. It only tells you that you can be 95% sure the results reported are reliable\.

## Blog Categories
- [Optimization](https://www.convert.com/blog/category/optimization/)
- [Growth Marketing](https://www.convert.com/blog/category/growth-marketing/)
- [A/B Testing](https://www.convert.com/blog/category/a-b-testing/)
- [Privacy](https://www.convert.com/blog/category/privacy/)
- [AI](https://www.convert.com/blog/category/ai/)

## Revisers
- [Sneh Ratna Choudhary](https://www.convert.com/post_revisers/sneh-ratna-choudhary/)

## Fact Checkers
- [Karim Naufal](https://www.convert.com/post_fact_checkers/karim/)
- [Poonam](https://www.convert.com/post_fact_checkers/poonam/)
- [Marcella Sullivan](https://www.convert.com/post_fact_checkers/marcella-sullivan/)
- [Ahmed](https://www.convert.com/post_fact_checkers/ahmed/)
- [Carmen Apostu](https://www.convert.com/post_fact_checkers/carmen-apostu/)

## Optional
- [Sitemap index](https://www.convert.com/sitemap_index.xml)