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Multiple testing vs sequential testing6/14/2023 ![]() If you are a business wanting to make data-driven decisions, your ability to iterate through the learning process will be directly tied to the amount of new changes you can introduce. Its unique approach combines sequential testing with false discovery rate control. Stats engine was built in combination with a team of statisticians at Standford university. Stats engine will give a company a competitive advantage compared to companies that only use classical statistic modelling methodologies in their experiments. Stats Engine is a massive jump forward in getting access to your experimentation data quicker and more accurately. ![]() Optimizely recognised this and in 2015 create a new type of stats framework, called Stats Engine. The important part of experimentation is the data. Running tests is great, however, tests on their own are pretty meaningless. The only way to be data-driven is through experimentation. As we all know, building something and then checking if customers will use it is the most expensive and time-consuming way to improve. Product investment can be spent only on building validated ideas. Being data-driven will mean no more wasted time and money building features that your customers do not want. Using painted door experiments in the design phase will allow you to validate an idea has merit. The way to get this data is to run experiments at all levels within your software development life cycle. In order for your company to make these data-driven decisions, you need to be able to capture the correct data. ![]() We have all heard of companies like Amazon and Netflix who have successfully used data-driven decisions to turn their companies into household names. In this tutorial, I will hopefully explain some of the magic behind Optimizely Stats engine framework.
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