Having trouble viewing the text? You can always read the original article here: Statistical Hypothesis Testing: A Simple Guide to Smarter A/B Tests A fundamental concept in A/B testing is statistical hypothesis testing. It involves creating a hypothesis about the relationship between two data sets and then comparing these data sets to determine if there is a statistically significant difference. It may sound complicated, but it explains how A/B testing works. Here’s a high-level look at how...| Conversion Sciences
Overgeneralization is a mistake in interpreting the outcomes of online controlled experiments (a.k.a. A/B tests) that can have a detrimental impact on any data-driven business. Overgeneralization is used in the typical sense of going above and beyond what the evidence at hand supports, with “evidence” being a statistically significant or non-significant outcome of an online […] Read more...| Blog for Web Analytics, Statistics and Data-Driven Internet Marketing | Analy...
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.| Blog for Web Analytics, Statistics and Data-Driven Internet Marketing | Analy...
What is Statistical Power?| Blog for Web Analytics, Statistics and Data-Driven Internet Marketing | Analy...
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%? Does it make sense to run tests at 50% significance? How about 5%? What is the cost of adding more variants to test?| Blog for Web Analytics, Statistics and Data-Driven Internet Marketing | Analy...
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.| Blog for Web Analytics, Statistics and Data-Driven Internet Marketing | Analy...
Short, understandable, yet accurate explanation of p-values and confidence intervals. Starting from the problem of random variability and building up with minimal jargon, this is the most accessible introduction to these basic statistical concepts. Understand the meaning and utility of confidence intervals and p-values in statistical hypothesis testing and estimation.| Blog for Web Analytics, Statistics and Data-Driven Internet Marketing | Analy...
The p-value is the most commonly used statistic in scientific papers and applied statistical analyses. Learn what its definition is, how to interpret it and how to calculate statistical significance if you are performing statistical tests of hypotheses. The utility, interpretation, and common misinterpretations of observed p-values and significance levels are illustrated with examples.| GIGAcalculator Articles
Navigating the maze of A/B testing statistics can be challenging. This is especially true for those new to statistics and probability. One reason is the obscure terminology popping up in every other sentence. Another is that the writings can be vague, conflicting, incomplete, or simply wrong, depending on the source. Articles sprinkled with advanced math, calculus equations, and poorly-labeled graphs represent a major hurdle for newcomers.| Blog for Web Analytics, Statistics and Data-Driven Internet Marketing | Analy...
Suppose I tell you that only 1% of people with COVID have a body temperature less than 97°. If you take someone's temperature and measure less than 97°, what| Life Is Computation