You can monitor the visitor’s activities using statistics and analysis to decide the version that yields a higher conversion rate. A/B Testing results are normally given in fancy **mathematical and statistical terms**, however the meaning behind the numbers are actually quite straightforward. There are two important methods through which you can check conversion rates using A/B Testing −

- Sampling of Data
- Confidence Intervals

Let us discuss these two above methods in detail.

## Sampling of Data

The number of samples depend upon the number of tests performed. The count of conversion rate is known as a sample and the process of collecting these samples is called as sampling.

### Example

Let us say you have two products A and B, you need to collect sample data as per its demand in the market. You can request a few people to choose from product A and B and then request them to participate in a survey. As the number of participant’s increase, it will begin showing a **realistic conversion rate**.

There are many different tools that can be used to determine the correct number of sample size. One such free tool available is −

http://www.evanmiller.org

## Confidence Intervals in A/B Testing

Confidence interval is the measurement of deviation from the normal on the multiple number of samples. Let us accept that 22% of people prefer product A in the above example, with ±2% of confidence interval. This interval indicates the upper and lower limit of the individuals, who opt for Product A and is also called as margin of error. For best outcomes in this average survey, **the margin of error should be as small as possible**.

### Example

Let us assume that in Product B, we have included a minor change and then performed A/B Testing on these two products. Confidence interval product A and B are 10% with ±1% and 20% with ±2% respectively. So this shows that a minor change has increased the conversion rate. If we ignore the margin of error, conversion rate for test variation A is 10% and conversion rate for test variation B is 20%, for example a 10% increase in the test variation.

Now, if we divide the difference by control variation rate 10% ÷ 10% = 1.0 = 100%, it shows an improvement of 100%. Hence, we can say that A/B Testing is a technique based on mathematical strategies and analysis. There are various online tools that can be utilized to calculate A/B significance.

http://getdatadriven.com