The A’s and B’s of A/B Testing — A Beginner’s guide to experimentation

The A’s and B’s of A/B Testing — A Beginner’s guide to experimentation

In this post, I am going to summarize my key learning from the course and explain how it benefits a lot of companies which are focused on improving user experience.

I recently completed Udacity’s course on A/B testing. The course started with a high-level understanding of what a typical A/B test entails before diving into specifics of each stage in the process of experimentation. Needless to say, it was a great learning experience! In this post, I am going to summarize my key learning from the course and explain how it benefits a lot of companies which are focused on improving user experience.

So, let us dive in!

So, tell me what is an A/B Test?

A/B testing is a method of experimentation to understand how user experiences change following any changes/variations made in the way they interact with a website, a mobile app etc. It is a very popular method to understand the level of existing customer experience and improve it based on certain relevant metrics.

Possible changes that companies may bring about are changing the layout of the website, changing the appearance/position of certain buttons on a particular webpage, change the ranking of options on the website etc. A/B testing helps the companies to evaluate whether these changes would be successful or not post-launch. This done by testing these changes on a subset of viewers for a certain period of time.

Okay, gotcha! But how will I evaluate these tests?

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Evaluation takes place on the basis of select metrics.

  1. Sanity Metrics: These are called invariant metrics. These are the metrics which should not change across control and experiment groups during the course of the experiment. If they change then there is something fundamentally wrong in the experiment setup.
  2. Evaluation metrics: These can be chosen on the basis of business needs. These can be any of the following listed below:

product-analytics data-science a-b-testing product-management experimentation big data

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