His model allowed Guinness to use more hops and reduce the time needed to check that the hops were up to scratch, improving their throughput.įast-forward 100 years, people are now building websites. This is what statistical significance is. His algorithm could be used to predict the number of samples required over a fixed period in order to mathematical guarantee that the quality assurance results would be accurate. In essence, he created an algorithm that allowed Guinness to run samples on their hops. Gosset comes in and creates the idea of statistical significance. They needed a new way to measure quality. Up until this point, Guinness used to manually sample large quantities of hops to ensure high quality, however, they realised that this process would block them from scaling. They wanted to create more of the black stuff, however, they did not want to impact quality. T TestingĪround 1900 Guinness was experiencing production issues. We start this journey talking about Guinness □☘️□. If I can get it, then I'm confident you can □. either that or my comprehension of maths is so low, I need my own special dummies guide to understand it! The purpose of this post is to give a plain English account of what stats engine does and why you could care. I am by no means a maths or stats guru and most of the articles that I have read on this topic seem to assume that you are. It is all very good saying that sequential testing and false discovery controls are clever, but, what does that actually mean?!?!?! This is where this article comes in. The takeaway is that being able to prove that your hypothesis is either correct or not is really important. Basically, the more sophisticated the stats engine, the more competitive advantage you will have over your competitors. 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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