a/b testing (analytics)
A/B testing is a cornerstone of data science, essential for making informed business decisions and optimizing customer revenue. Here, we delve into six widely used statistical methods in A/B testing, explaining their purposes and appropriate contexts. 1. Z-Test (Standard Score Test): When to Use: This method is ideal for large sample sizes (typically over 30) when the population variance is known. Purpose: Compares the means of two groups to determine if they are statistically different. Applications: This technique is frequently employed in conversion rate optimization and click-through rate analysis. It helps identify whether changes in website elements or marketing strategies
Visual, interactive sample size calculator ideal for planning online experiments and A/B tests.
Running experiments is equal parts powerful and terrifying. Powerful because you can validate changes that will transform your product for the better…
A/B tests provide more than statistical validation of one execution over another. They can and should impact how your team prioritizes projects.
We’re Agile, we think lean, we’re data-driven. If you live in the new economy and work in some sort of digital product you hear some of…
An applied introduction to causal inference in tech
We spoke with Etsy’s iOS Software Engineer, Lacy Rhoades, about their culture of continuous experimentation. Learn about their a/b testing culture
A/B testing is a very popular technique of checking granular changes in a product without mistakenly taking into account changes that were…
Multivariate tests indicate how various UI elements interact with each other and are a tool for making incremental improvements to a design.
The best way to determine what works best for your site is to carry out an A/B test for your landing pages. Check out this A/B significant test calculator.
Elaborate usability tests are a waste of resources. The best results come from testing no more than 5 users and running as many small tests as you can afford.
A/B testing, the process of exposing randomized visitors to one or more variables, is among the most effective strategies to optimize user experiences and conversion rates. Here is a list of A/B testing tools.
The best way to optimise your website is usually the simplest.
A/B Testing — A complete guide to statistical testing - bjpcjp/AB_Testing
Big success. Bigger failure. And lots of lessons. Learn why building a growth team may be a multi-million dollar mistake.
A/B tests are controlled experiments of two attributes, to measure which one was most popular with users. You can apply A/B testing to just about anything that you can measure. Multivariate testing allows you to measure multiple variables simultaneously.
Data Science, Machine Learning, AI & Analytics