In the Modern Digital Era Universe, A/B Testing is the true and best method to take UX Concepts to real time after a test drive. This online experiments will validate your strongest hypothesis rigorously before     turning an idea into reality. This should be done in a perfect manner else it hurts a lot rather than help your business,

We need to keep four key factors in to mind when it comes to research methodlogy of A/B Testing.

1. The best way to tell if your A/B Testing is successful is to create a holdout group which will measure the combined effect of all product development and to establish the relationship between the changes and performance of the company. It's easy to spot weak points and highlight the strenghts. 

The holdout group is a powerful tool to detect issues with the A/B Testing system after the experiment is scaled. They are a small percentage of customers who do not participate. Need to Implement all the Technical Possibilities that maintains the frozen version of the product. 

Need to monitor the performance  on  a regular basis and need to understand the methodology behind implementing with the division of the audience into one specific segment..The range of this group varies from 1% to 10% of the entire audience. It might e the comination of Control (47.5%), Variation(47.5%) and Holdout (5%).

We need to analyze one thing that how a new feature help to grow the business? It is vital for companies to make sure that product doesn't become unnecessarily complex over the time and it is known that scaling features brings value to the customers. The best approach is to make sure that your scale ratio is higher than your p-value.It helps to prevent developing unnecessary features for this product.

Need to set goals, expectations and benchmarks for success by connecting A/B testing with the performance of the company.Need to clearly distinguish between UI(User Interface)/UX( User Experience) designs before testing. In order to generate quality web traffic we need to focus on and increase the conversion rate optimization (CRO).CRO can also help uncover why customers maynot act once they visit your website which is a critical and important research that can inform your A/B testing.CRO is a binomial metric that lies between Zero and One that helps to get faster results.

Right from start to finish you need to have a deeper understanding of the customer journey process.This will let you know the priorities and key moments where you will know enhancements and improvements take place in the website.

When it comes to A/B testing organizations need to provide frameworks that remove the possibility of bias.We need to go to project with strong hypothesis and clear metrics for improvement for the smallest improvement is detected when Minimum detectable effect (MDE) isn't set. The easiest way to identify biases is to look for exceptions.Bias can happen when multiple tests are conducted once and are not managed correctly. We need to set up individual tests (Easiest way to go),multi-variant tests ( a combination of two ojectives) or mutually exclusive tests(simultaneous bbut separate) properly to avoid bias.

It is very much needed to identify the broken functionality in A/B testing. There are several numerous points where A/B testing can go wrong. Sample Ratio Mismatch (SRM) happens when your split is incorrect.. The most common problem is having a technical mistake ot the wrong  point of  split, Another reason is using incorrect data which leads to Internal Server. We need to run A/B tests reguarly to see if the results can be replicated. You must uncover the issues within your framework which prevents you from being data-driven. 

While validating some results may be false positives. If your doing right, the percentage of false positive A/B tests should be equal to p-value. Error instrumentation will also be added by companies which will help them in finding the errors when launching a new experiment,When you don't have enough traffic then split A/B testing is not the right choice. It will take time to collect the data needed to kick start A/B testing. Need to wait till you have enough visitors to justify the test.

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