# Articles onStatistics

Articles on statistics written for marketers, user experience specialists, and web analysists in mind. Digging deep into statistics for online experiments (a.k.a. online A/B tests) and the methodological issues and approaches for solving them.

## The Google Optimize Statistical Engine and Approach

Updated Sep 17, 2018: Minor spelling and language corrections, updates related to role of randomization and external validity / generalizability. Google Optimize is the latest attempt from Google to deliver an A/B testing product. Previously we had “Google Website Optimizer”, then we had “Content Experiments” within Google Analytics, and now we have the latest iteration: […] Read more…

## 20-80% Faster A/B Tests? Is it real?

I got a question today about our AGILE A/B testing calculator and the statistics behind it and realized that I’m yet to write a dedicated post explaining the efficiency gains from using the method in more detail. This despite the fact that these speed gains are clearly communicated and verified through simulation results presented in our AGILE […] Read more…

## Risk vs. Reward in A/B Tests: A/B testing as Risk Management

What is the goal of A/B testing? How long should I run a test for? Is it better to run many quick tests, or one long one? How do I know when is a good time to stop testing? How do I choose the significance threshold for a test? Is there something special about 95%? […] Read more…

## Statistical Significance for Non-Binomial Metrics – Revenue per User, AOV, etc.

In this article I cover the method required to calculate statistical significance for non-binomial metrics such as average revenue per user, average order value, average sessions per user, average session duration, average pages per session, and others. The focus is on A/B testing in the context of conversion rate optimization, landing page optimization and e-mail […] Read more…

## One-tailed vs Two-tailed Tests of Significance in A/B Testing

The question of whether one should run A/B tests (a.k.a online controlled experiments) using one-tailed versus two-tailed tests of significance was something I didn’t even consider important, as I thought the answer (one-tailed) was so self-evident that no discussion was necessary. However, while preparing for my course on “Statistics in A/B Testing” for the ConversionXL […] Read more…

Also posted in A/B testing, Conversion optimization, Statistical significance |

## The Case for Non-Inferiority A/B Tests

In this article, I explore the concept of non-inferiority A/B tests and contrast it to the broadly accepted practice of running superiority tests. I explain where non-inferiority tests are necessary and how a CRO/LPO/UX testing specialist can make use of this new approach to A/B testing to run much faster tests, and to ultimately achieve […] Read more…

## Statistical Significance in A/B Testing – a Complete Guide

The concept of statistical significance is central to planning, executing and evaluating A/B (and multivariate) tests, but at the same time it is the most misunderstood and misused statistical tool in internet marketing, conversion optimization, landing page optimization, and user testing. This article attempts to lay it out in as plain English as possible: covering […] Read more…

## Multivariate Testing – Best Practices & Tools for MVT (A/B/n) Tests

Let’s get this out of the way from the very beginning: most “A/B tests” are in fact multivariate (MVT) tests, a.k.a. A/B/n tests. That is, most of the time when you read about “A/B testing” the term also encompasses multivariate testing. The only reason to specifically differentiate between A/B and MVT is when someone wants to […] Read more…

## Futility Stopping Rules in AGILE A/B Testing

In this article we continue our examination of the AGILE statistical approach to AB testing with a more in-depth look into futility stopping, or stopping early for lack of positive effect (lack of superiority). We’ll cover why such rules are helpful and how they help boost the ROI of A/B testing, why a rigorous statistical rule […] Read more…

Also posted in A/B testing, AGILE A/B testing, Conversion optimization |

## Efficient AB Testing with the AGILE Statistical Method

Don’t we all want to run tests as quickly as possible, reaching results as conclusive and as certain as possible? Don’t we all want to minimize the number of users we send to an inferior variant and to implement a variant with positive lift as quickly as possible? Don’t we all want to get rid of […] Read more…

## Improving ROI in A/B Testing: the AGILE AB Testing Approach

After many months of statistical research and development we are happy to announce two major releases that we believe have the potential to reshape statistical practice in the area of A/B testing by substantially increasing the accuracy, efficiency and ultimately return on investment of all kinds of A/B testing efforts in online marketing: a free white […] Read more…

## The Importance of Statistical Power in Online A/B Testing

What is Statistical Power? In null-hypothesis statistical testing (NHST) – the procedure most commonly applied in A/B tests, there are two types of errors that practitioners should care about, type I and type II errors. Type I is the probability of the test procedure to falsely reject a true null hypothesis. Type II error is […] Read more…

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