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.
Evaluating ad targeting product using causal inference: propensity score matching!
For non-tech industry folks, an “A/B test” is just a randomized controlled trial where you split users or other things into treatment and control groups, and then later compare key metr…
Running experiments is equal parts powerful and terrifying. Powerful because you can validate changes that will transform your product for the better…
An in-depth explanation of “Thompson Sampling”, a more efficient alternative to A/B testing for online learning
Questions on A/B testing are being increasingly asked in interviews but reliable resources to prepare for these are still far and few…
Using and choosing priors in randomized experiments.
A Discussion of the go-to methods for 5 Types of A/B Metrics
How today’s tech companies make data-driven decisions in Machine Learning production
While Fisher’s exact test is a convenient tool for A/B testing, the idea and results of the test are often hard to grasp and difficult to…
A/B testing is hitting the mainstream because it is so effective. And with so many tools available it has become very easy and very inexpensive to run. Here are 23 helpful tips on how you can take your A/B tests from basic to the next level.
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.
Our A/B test calculator will help you to compare two or three variants to determine which test will be statistically significant.
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.
Statistics & Business can share the same Language
Experimentation is widely used at tech startups to make decisions on whether to roll out new product features, UI design changes, marketing campaigns and more, usually with the goal of improving…
The best way to optimise your website is usually the simplest.
How not to fail your online controlled experimentation
Optimizing web marketing strategies through statistical testing
A/B Testing — A complete guide to statistical testing - bjpcjp/AB_Testing
The intuitive way of A/B testing. The advantages of the Bayesian approach and how to do it.
Big success. Bigger failure. And lots of lessons. Learn why building a growth team may be a multi-million dollar mistake.
The biggest question in ecommerce A/B testing is not “how.”
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