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A/B Test Results Analysis: A Comprehensive Guide
Author: Dr. Evelyn Reed, PhD in Statistics, 10+ years experience in data analysis and A/B testing for e-commerce and SaaS companies.
Publisher: DataDriven Insights, a leading publisher of data analysis and business intelligence resources.
Editor: Mark Johnson, MA in Business Analytics, 7+ years experience editing publications on data science and marketing analytics.
Keywords: A/B test results analysis, statistical significance, conversion rate, click-through rate, A/B testing, hypothesis testing, p-value, confidence interval, Bayesian A/B testing, multivariate testing, sample size, power analysis.
Abstract: This article provides a comprehensive guide to A/B test results analysis, covering various methodologies and approaches to ensure accurate interpretation and actionable insights. We'll explore statistical significance, practical significance, different testing methods, and common pitfalls to avoid. Effective A/B test results analysis is crucial for data-driven decision making.
1. Understanding the Fundamentals of A/B Test Results Analysis
A/B testing, also known as split testing, is a crucial methodology for improving website conversion rates, user experience, and overall business performance. The core of A/B test results analysis lies in determining whether the observed differences between the control (A) and variation (B) groups are statistically significant and practically meaningful. A/B test results analysis goes beyond simply observing differences; it involves rigorous statistical methods to quantify the likelihood that these differences are due to chance or represent a genuine improvement.
2. Statistical Significance in A/B Test Results Analysis
The cornerstone of A/B test results analysis is establishing statistical significance. This involves using statistical tests, typically hypothesis testing, to determine the probability (p-value) that the observed difference between the control and variation groups occurred by random chance. A low p-value (typically below 0.05) suggests strong evidence against the null hypothesis (that there's no difference between the groups), indicating statistical significance. However, simply achieving statistical significance isn't enough; we must also consider practical significance.
3. Practical Significance in A/B Test Results Analysis
While statistical significance shows a real difference, practical significance determines whether that difference is large enough to be meaningful for your business. A statistically significant difference might be negligible in terms of revenue or conversion rate improvement. Consider the magnitude of the difference (effect size) and its impact on your key performance indicators (KPIs) when analyzing A/B test results.
4. Choosing the Right Statistical Test for A/B Test Results Analysis
The appropriate statistical test depends on the type of data you're analyzing. Common tests include:
Z-test: Used for comparing proportions (e.g., conversion rates) with large sample sizes.
t-test: Used for comparing means (e.g., average order value) with smaller sample sizes.
Chi-square test: Used for analyzing categorical data (e.g., click-through rates on different buttons).
Selecting the wrong test can lead to inaccurate A/B test results analysis and flawed conclusions.
5. Confidence Intervals in A/B Test Results Analysis
Confidence intervals provide a range of values within which the true difference between the groups is likely to fall with a certain level of confidence (e.g., 95%). A narrow confidence interval suggests more precise estimation, while a wide interval indicates more uncertainty. Analyzing confidence intervals alongside p-values provides a more complete picture of the A/B test results.
6. Sample Size and Power Analysis in A/B Test Results Analysis
Adequate sample size is crucial for reliable A/B test results analysis. Insufficient samples can lead to false negatives (missing true differences) or false positives (detecting differences that don't exist). Power analysis helps determine the required sample size to detect a meaningful difference with a desired level of power (the probability of correctly rejecting the null hypothesis when it's false). Careful planning before the test is key for accurate A/B test results analysis.
7. Bayesian A/B Testing: A Different Approach to A/B Test Results Analysis
While frequentist methods (like z-tests and t-tests) focus on p-values, Bayesian A/B testing utilizes Bayes' theorem to update beliefs about the probability of different outcomes based on observed data. This approach allows for incorporating prior knowledge and provides a probability distribution for the difference between the groups, rather than a single p-value. Bayesian A/B test results analysis can be particularly useful when dealing with limited data or incorporating expert opinions.
8. Multivariate Testing and A/B Test Results Analysis
Multivariate testing allows for simultaneous testing of multiple variations of different elements (e.g., headlines, images, calls to action). Analyzing results from multivariate tests requires more sophisticated statistical methods and careful consideration of interactions between different elements. Multivariate testing provides a more comprehensive understanding of the impact of different website changes but also increases the complexity of A/B test results analysis.
9. Avoiding Common Pitfalls in A/B Test Results Analysis
Several common pitfalls can lead to inaccurate interpretations of A/B test results. These include:
Ignoring practical significance: Focusing solely on statistical significance without considering the practical impact.
Incorrect statistical test selection: Choosing the wrong statistical test for the data type.
Insufficient sample size: Leading to unreliable results.
Premature termination: Stopping the test before sufficient data is collected.
P-hacking: Manipulating the data or analysis to achieve statistical significance.
Careful planning, rigorous analysis, and awareness of these pitfalls are essential for accurate A/B test results analysis.
Conclusion
Effective A/B test results analysis is critical for data-driven decision-making. By understanding statistical significance, practical significance, appropriate statistical tests, and potential pitfalls, you can leverage A/B testing to optimize your website, improve user experience, and drive business growth. Remember that A/B test results analysis isn't a one-size-fits-all process; adapting your approach to the specific context of your experiment is crucial for deriving meaningful and actionable insights.
FAQs
1. What is the difference between statistical significance and practical significance? Statistical significance indicates a real difference between groups, while practical significance determines if that difference is large enough to matter.
2. What is the appropriate sample size for an A/B test? The required sample size depends on the effect size, desired power, and significance level. Power analysis helps determine this.
3. How do I choose the right statistical test for my A/B test? The choice depends on the type of data (categorical or continuous) and sample size. Z-tests, t-tests, and chi-square tests are common choices.
4. What is a confidence interval, and why is it important? A confidence interval provides a range of values within which the true difference likely lies, indicating the precision of the estimate.
5. What are the common pitfalls to avoid in A/B test results analysis? Ignoring practical significance, incorrect test selection, insufficient sample size, premature termination, and p-hacking are major pitfalls.
6. What is Bayesian A/B testing, and how does it differ from frequentist methods? Bayesian A/B testing incorporates prior knowledge and provides a probability distribution for the difference, unlike frequentist methods that focus on p-values.
7. How do I interpret a p-value in the context of A/B testing? A low p-value (usually below 0.05) suggests strong evidence against the null hypothesis (no difference), indicating statistical significance.
8. What is multivariate testing, and how does it differ from A/B testing? Multivariate testing involves testing multiple variations of multiple elements simultaneously, whereas A/B testing typically compares two versions.
9. How can I ensure the accuracy of my A/B test results analysis? Careful planning, appropriate statistical methods, sufficient sample size, and awareness of potential biases are crucial for accuracy.
Related Articles:
1. "Understanding P-values and Statistical Significance in A/B Testing": This article explains the concepts of p-values and statistical significance in detail, providing clear examples and practical guidance.
2. "Choosing the Right Statistical Test for Your A/B Test": A guide to selecting the appropriate statistical test based on the type of data and experimental design.
3. "Power Analysis for A/B Testing: Determining the Optimal Sample Size": This article focuses on power analysis and its importance in determining the necessary sample size for reliable results.
4. "Bayesian A/B Testing: A Comprehensive Introduction": An in-depth exploration of Bayesian A/B testing methods and their advantages over frequentist approaches.
5. "Multivariate Testing: A Powerful Technique for Website Optimization": This article discusses the principles and applications of multivariate testing, including its advantages and challenges.
6. "Interpreting Confidence Intervals in A/B Test Results": This article explains how to interpret confidence intervals and how they contribute to a more nuanced understanding of A/B testing results.
7. "Common Mistakes to Avoid When Conducting A/B Tests": This article highlights the most common errors in A/B testing and offers practical solutions to avoid them.
8. "A/B Testing Best Practices: From Design to Analysis": A comprehensive guide covering all aspects of A/B testing, from experimental design to result interpretation.
9. "Advanced A/B Testing Techniques: Beyond the Basics": This article delves into more advanced topics such as sequential testing and incorporating user segmentation into A/B testing analysis.
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| ab test results analysis: Your Customer Creation Equation Brian Massey, 2012-07-01 Finally-a book that shows marketers how to truly achieve real results from their websites. Brian Massey, The Conversion Scientist, takes the mystery out of how to create high-performing sites. By walking the reader through five online formulas-aka customer creation equations-he shows you how to determine the best formula your own particular business structure and how to optimize it for stellar results. Key to this process is setting up a digital conversion lab, and Brian shows you how. Jam-packed with easy-to-understand equations for things like increasing your conversion rate and decreasing your abandonment rate-as well as practical strategies for attracting prospects, turning buyers into triers, and morphing buyers into loyal brand advocates-this book will enable anyone to stop hoping for success and start enjoying higher profits. The Advanced Curriculum in Visitor Studies gives readers additional guidance on how to really understand their targets and customers-an understanding that is at the heart of all successful websites, and businesses, everywhere. |
| ab test results analysis: A / B Testing Dan Siroker, Pete Koomen, 2015-07-27 How Your Business Can Use the Science That Helped Win the White House The average conversion rate—the rate at which visitors convert into customers—across the web is only 2%. That means it's likely that 98% of visitors to your website won't end up converting into customers. What's the solution? A/B testing. A/B testing is the simple idea of showing several different versions of a web page to live traffic, and then measuring the effect each version has on visitors. Using A/B testing, companies can improve the effectiveness of their marketing and user experience and, in doing so, can sometimes double or triple their conversion rates. Testing has been fundamental in driving the success of Google, Amazon, Netflix, and other top tech companies. Even Barack Obama and Mitt Romney had dedicated teams A/B testing their campaign websites during the 2012 Presidential race. In the past, marketing teams were unable to unleash the power of A/B testing because it required costly engineering and IT resources. Today, a new generation of technology that enables marketers to run A/B tests without depending on engineers is emerging and quickly becoming one of the most powerful tools for making data-driven decisions. Authors Dan Siroker and Pete Koomen are cofounders of Optimizely, the leading A/B testing platform used by more than 5,000 organizations across the world. A/B Testing: The Most Powerful Way to Turn Clicks Into Customers offers best practices and lessons learned from more than 300,000 experiments run by Optimizely customers. You'll learn: What to test How to choose the testing solution that's right for your organization How to assemble an A/B testing dream team How to create personalized experiences for every visitor And much more Marketers and web professionals will become obsolete if they don't embrace a data-driven approach to decision making. This book shows you how, no matter your technical expertise. |
| ab test results analysis: Practical A/B Testing Leemay Nassery, 2023-05-23 Whether you're a catalyst for organizational change or have the support you need to create an engineering culture that embraces A/B testing, this book will help you do it right. The step-by-step instructions will demystify the entire process, from constructing an A/B test to breaking down the decision factors to build an engineering platform. When you're ready to run the A/B test of your dreams, you'll have the perfect blueprint. With smart, tactful approaches to orchestrating A/B testing on a product, you'll quickly discover how to reap all the benefits that A/B testing has to offer - benefits that span your users, your product, and your team. Take the reins today, and be the change you want to see in your engineering and product organizations. Develop a hypothesis statement that's backed with metrics that demonstrate if your prediction for the experiment is correct. Build more inclusive products by leveraging audience segmentation strategies and ad-hoc post analysis to better understand the impact of changes on specific user groups. Determine which path is best for your team when deciding whether to go with a third-party A/B test framework or to build the A/B testing platform in-house. And finally, learn how to cultivate an experimentation-friendly culture within your team. Leverage the A/B testing methodology to demonstrate the impact of changes on a product to your users, your key business metrics, and the way your team works together. After all, if you aren't measuring the impact of the changes you make, how will you know if you're truly making improvements? |
| ab test results analysis: Conversion Optimization Khalid Saleh, Ayat Shukairy, 2010-11-01 How do you turn website visitors into customers? Conversion Optimization offers practical advice on how to persuade visitors to make a buying decision -- without driving them away through data overload or tedious navigation. You'll learn how to use marketing principles, design, usability, and analytics on your site to increase your buyer-to-visitor ratio, whether you're involved with marketing or designing a large ecommerce site, or managing a modest online operation. Based on the authors' broad experience in helping businesses attract online customers, this book addresses every aspect of the process, from landing visitors to finalizing the sale. You'll learn several techniques for blending successful sales approaches with the particular needs of the people you want to attract. Are you ready to do what it takes to get a double-digit conversion rate? Explore case studies involving significant conversion rate improvements Walk through different stages of a sale and understand the value of each Understand your website visitors through persona creation Connect with potential customers and guide them toward a conversion Learn how to deal with FUDs -- customer fears, uncertainties, and doubts Examine the path that visitors take from landing page to checkout Test any change you make against your original design The Web is unique in its ability to deliver this almost improbable win-win: You can increase revenue AND make your customers happy. Yet most websites stink. Worry not, Khalid and Ayat to the rescue! Buy this book to follow their practical advice on how to create high converting websites that your visitors love.--Avinash Kaushik, author of Web Analytics 2.0 and Web Analytics: An Hour A Day (both Sybex) |
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| ab test results analysis: Bandit Algorithms for Website Optimization John Myles White, 2012-12-10 When looking for ways to improve your website, how do you decide which changes to make? And which changes to keep? This concise book shows you how to use Multiarmed Bandit algorithms to measure the real-world value of any modifications you make to your site. Author John Myles White shows you how this powerful class of algorithms can help you boost website traffic, convert visitors to customers, and increase many other measures of success. This is the first developer-focused book on bandit algorithms, which were previously described only in research papers. You’ll quickly learn the benefits of several simple algorithms—including the epsilon-Greedy, Softmax, and Upper Confidence Bound (UCB) algorithms—by working through code examples written in Python, which you can easily adapt for deployment on your own website. Learn the basics of A/B testing—and recognize when it’s better to use bandit algorithms Develop a unit testing framework for debugging bandit algorithms Get additional code examples written in Julia, Ruby, and JavaScript with supplemental online materials |
| ab test results analysis: Experimentation for Engineers David Sweet, 2023-03-21 Optimize the performance of your systems with practical experiments used by engineers in the world’s most competitive industries. In Experimentation for Engineers: From A/B testing to Bayesian optimization you will learn how to: Design, run, and analyze an A/B test Break the feedback loops caused by periodic retraining of ML models Increase experimentation rate with multi-armed bandits Tune multiple parameters experimentally with Bayesian optimization Clearly define business metrics used for decision-making Identify and avoid the common pitfalls of experimentation Experimentation for Engineers: From A/B testing to Bayesian optimization is a toolbox of techniques for evaluating new features and fine-tuning parameters. You’ll start with a deep dive into methods like A/B testing, and then graduate to advanced techniques used to measure performance in industries such as finance and social media. Learn how to evaluate the changes you make to your system and ensure that your testing doesn’t undermine revenue or other business metrics. By the time you’re done, you’ll be able to seamlessly deploy experiments in production while avoiding common pitfalls. About the technology Does my software really work? Did my changes make things better or worse? Should I trade features for performance? Experimentation is the only way to answer questions like these. This unique book reveals sophisticated experimentation practices developed and proven in the world’s most competitive industries that will help you enhance machine learning systems, software applications, and quantitative trading solutions. About the book Experimentation for Engineers: From A/B testing to Bayesian optimization delivers a toolbox of processes for optimizing software systems. You’ll start by learning the limits of A/B testing, and then graduate to advanced experimentation strategies that take advantage of machine learning and probabilistic methods. The skills you’ll master in this practical guide will help you minimize the costs of experimentation and quickly reveal which approaches and features deliver the best business results. What's inside Design, run, and analyze an A/B test Break the “feedback loops” caused by periodic retraining of ML models Increase experimentation rate with multi-armed bandits Tune multiple parameters experimentally with Bayesian optimization About the reader For ML and software engineers looking to extract the most value from their systems. Examples in Python and NumPy. About the author David Sweet has worked as a quantitative trader at GETCO and a machine learning engineer at Instagram. He teaches in the AI and Data Science master's programs at Yeshiva University. Table of Contents 1 Optimizing systems by experiment 2 A/B testing: Evaluating a modification to your system 3 Multi-armed bandits: Maximizing business metrics while experimenting 4 Response surface methodology: Optimizing continuous parameters 5 Contextual bandits: Making targeted decisions 6 Bayesian optimization: Automating experimental optimization 7 Managing business metrics 8 Practical considerations |
| ab test results analysis: Forecasting: principles and practice Rob J Hyndman, George Athanasopoulos, 2018-05-08 Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly. |
| ab test results analysis: Doing Bayesian Data Analysis John Kruschke, 2010-11-25 There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis tractable and accessible to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS, is for first year graduate students or advanced undergraduates and provides an accessible approach, as all mathematics is explained intuitively and with concrete examples. It assumes only algebra and 'rusty' calculus. Unlike other textbooks, this book begins with the basics, including essential concepts of probability and random sampling. The book gradually climbs all the way to advanced hierarchical modeling methods for realistic data. The text provides complete examples with the R programming language and BUGS software (both freeware), and begins with basic programming examples, working up gradually to complete programs for complex analyses and presentation graphics. These templates can be easily adapted for a large variety of students and their own research needs.The textbook bridges the students from their undergraduate training into modern Bayesian methods. - Accessible, including the basics of essential concepts of probability and random sampling - Examples with R programming language and BUGS software - Comprehensive coverage of all scenarios addressed by non-bayesian textbooks- t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis). - Coverage of experiment planning - R and BUGS computer programming code on website - Exercises have explicit purposes and guidelines for accomplishment |
| ab test results analysis: Statistical Analysis Quick Reference Guidebook Alan C. Elliott, Wayne A. Woodward, 2007 A practical `cut to the chase′ handbook that quickly explains the when, where, and how of statistical data analysis as it is used for real-world decision-making in a wide variety of disciplines. In this one-stop reference, the authors provide succinct guidelines for performing an analysis, avoiding pitfalls, interpreting results and reporting outcomes. |
| ab test results analysis: The Five Love Languages Gary Chapman, 2009-12-17 Marriage should be based on love, right? But does it seem as though you and your spouse are speaking two different languages? #1 New York Times bestselling author Dr. Gary Chapman guides couples in identifying, understanding, and speaking their spouse's primary love language-quality time, words of affirmation, gifts, acts of service, or physical touch. By learning the five love languages, you and your spouse will discover your unique love languages and learn practical steps in truly loving each other. Chapters are categorized by love language for easy reference, and each one ends with simple steps to express a specific language to your spouse and guide your marriage in the right direction. A newly designed love languages assessment will help you understand and strengthen your relationship. You can build a lasting, loving marriage together. Gary Chapman hosts a nationally syndicated daily radio program called A Love Language Minute that can be heard on more than 150 radio stations as well as the weekly syndicated program Building Relationships with Gary Chapman, which can both be heard on fivelovelanguages.com. The Five Love Languages is a consistent New York Times bestseller - with over 5 million copies sold and translated into 38 languages. This book is a sales phenomenon, with each year outselling the prior for 16 years running! |
| ab test results analysis: Mastering Shiny Hadley Wickham, 2021-04-29 Master the Shiny web framework—and take your R skills to a whole new level. By letting you move beyond static reports, Shiny helps you create fully interactive web apps for data analyses. Users will be able to jump between datasets, explore different subsets or facets of the data, run models with parameter values of their choosing, customize visualizations, and much more. Hadley Wickham from RStudio shows data scientists, data analysts, statisticians, and scientific researchers with no knowledge of HTML, CSS, or JavaScript how to create rich web apps from R. This in-depth guide provides a learning path that you can follow with confidence, as you go from a Shiny beginner to an expert developer who can write large, complex apps that are maintainable and performant. Get started: Discover how the major pieces of a Shiny app fit together Put Shiny in action: Explore Shiny functionality with a focus on code samples, example apps, and useful techniques Master reactivity: Go deep into the theory and practice of reactive programming and examine reactive graph components Apply best practices: Examine useful techniques for making your Shiny apps work well in production |
| ab test results analysis: Cochrane Handbook for Systematic Reviews of Interventions Julian P. T. Higgins, Sally Green, 2008-11-24 Healthcare providers, consumers, researchers and policy makers are inundated with unmanageable amounts of information, including evidence from healthcare research. It has become impossible for all to have the time and resources to find, appraise and interpret this evidence and incorporate it into healthcare decisions. Cochrane Reviews respond to this challenge by identifying, appraising and synthesizing research-based evidence and presenting it in a standardized format, published in The Cochrane Library (www.thecochranelibrary.com). The Cochrane Handbook for Systematic Reviews of Interventions contains methodological guidance for the preparation and maintenance of Cochrane intervention reviews. Written in a clear and accessible format, it is the essential manual for all those preparing, maintaining and reading Cochrane reviews. Many of the principles and methods described here are appropriate for systematic reviews applied to other types of research and to systematic reviews of interventions undertaken by others. It is hoped therefore that this book will be invaluable to all those who want to understand the role of systematic reviews, critically appraise published reviews or perform reviews themselves. |
| ab test results analysis: Applied Power Analysis for the Behavioral Sciences Christopher L. Aberson, 2019-01-24 Applied Power Analysis for the Behavioral Sciences is a practical how-to guide to conducting statistical power analyses for psychology and related fields. The book provides a guide to conducting analyses that is appropriate for researchers and students, including those with limited quantitative backgrounds. With practical use in mind, the text provides detailed coverage of topics such as how to estimate expected effect sizes and power analyses for complex designs. The topical coverage of the text, an applied approach, in-depth coverage of popular statistical procedures, and a focus on conducting analyses using R make the text a unique contribution to the power literature. To facilitate application and usability, the text includes ready-to-use R code developed for the text. An accompanying R package called pwr2ppl (available at https://github.com/chrisaberson/pwr2ppl) provides tools for conducting power analyses across each topic covered in the text. |
| ab test results analysis: Regression Methods in Biostatistics Eric Vittinghoff, David V. Glidden, Stephen C. Shiboski, Charles E. McCulloch, 2012 This fresh edition, substantially revised and augmented, provides a unified, in-depth, readable introduction to the multipredictor regression methods most widely used in biostatistics. The examples used, analyzed using Stata, can be applied to other areas. |
| ab test results analysis: Landing Page Optimization Tim Ash, Maura Ginty, Rich Page, 2012-03-29 A fully updated guide to making your landing pages profitable Effective Internet marketing requires that you test and optimize your landing pages to maximize exposure and conversion rate. This second edition of a bestselling guide to landing page optimization includes case studies with before-and-after results as well as new information on web site usability. It covers how to prepare all types of content for testing, how to interpret results, recognize the seven common design mistakes, and much more. Included is a gift card for Google AdWords. Features fully updated information and case studies on landing page optimization Shows how to use Google's Website Optimizer tool, what to test and how to prepare your site for testing, the pros and cons of different test strategies, how to interpret results, and common site design mistakes Provides a step-by-step implementation plan and advice on getting support and resources Landing Page Optimization, Second Edition is a comprehensive guide to increasing conversions and improving profits. |
| ab test results analysis: You Should Test That Chris Goward, 2012-12-21 Learn how to convert website visitors into customers Part science and part art, conversion optimization is designed to turn visitors into customers. Carefully developed testing procedures are necessary to help you fine-tune images, headlines, navigation, colors, buttons, and every other element, creating a website that encourages visitors to take the action you seek. This book guides you through creating an optimization strategy that supports your business goals, using appropriate analytics tools, generating quality testing ideas, running online experiments, and making the adjustments that work. Conversion optimization is part science and part art; this guide provides step-by-step guidance to help you optimize your website for maximum conversion rates Explains how to analyze data, prioritize experiment opportunities, and choose the right testing methods Helps you learn what to adjust, how to do it, and how to analyze the results Features hands-on exercises, case studies, and a full-color insert reinforcing key tactics Author has used these techniques to assist Fortune 500 clients You Should Test That explains both the why and the how of conversion optimization, helping you maximize the value of your website. |
| ab test results analysis: E-Commerce Website Optimization Dan Croxen-John, Johann van Tonder, 2020-12-03 Conversion rate optimization (CRO) is about understanding people and behaviour, not simply website visits. This book is an all-encompassing guide to the how, the why and the tools and techniques. Grounded in best-practice theory and research, E-commerce Website Optimization brings together usability, analytics and persuasion to offer a detailed, step-by-step guide to increasing the percentage of visitors who buy from your site and subsequently the amount that these visitors spend when they do. In a complex and evolving field, it will help you improve conversion rates, increase ROI from online marketing campaigns, generate higher levels of repeat business and increase the e-commerce value of websites. In the fast-moving world of e-commerce, this fully revised second edition includes updates on test metrics, prioritization and personalization, alongside updated case studies and newly recommended tools. E-commerce Website Optimization is an invaluable book for those seeking to implement a data-driven ethos for their organization's e-commerce programme, for everyone from chief digital officers and heads of online sales, to entrepreneurs and small business owners. |
| ab test results analysis: Guidelines on Hepatitis B and C Testing World Health Organization, 2017 Testing and diagnosis of hepatitis B (HBV) and C (HCV) infection is the gateway for access to both prevention and treatment services, and is a crucial component of an effective response to the hepatitis epidemic. Early identification of persons with chronic HBV or HCV infection enables them to receive the necessary care and treatment to prevent or delay progression of liver disease. Testing also provides an opportunity to link people to interventions to reduce transmission, through counselling on risk behaviors and provision of prevention commodities (such as sterile needles and syringes) and hepatitis B vaccination. These are the first WHO guidelines on testing for chronic HBV and HCV infection and complement published guidance by WHO on the prevention, care and treatment of chronic hepatitis C and hepatitis B infection. These guidelines outline the public health approach to strengthening and expanding current testing practices for HBV and HCV, and are intended for use across age groups and populations. |
| ab test results analysis: Pain Management and the Opioid Epidemic National Academies of Sciences, Engineering, and Medicine, Health and Medicine Division, Board on Health Sciences Policy, Committee on Pain Management and Regulatory Strategies to Address Prescription Opioid Abuse, 2017-10-28 Drug overdose, driven largely by overdose related to the use of opioids, is now the leading cause of unintentional injury death in the United States. The ongoing opioid crisis lies at the intersection of two public health challenges: reducing the burden of suffering from pain and containing the rising toll of the harms that can arise from the use of opioid medications. Chronic pain and opioid use disorder both represent complex human conditions affecting millions of Americans and causing untold disability and loss of function. In the context of the growing opioid problem, the U.S. Food and Drug Administration (FDA) launched an Opioids Action Plan in early 2016. As part of this plan, the FDA asked the National Academies of Sciences, Engineering, and Medicine to convene a committee to update the state of the science on pain research, care, and education and to identify actions the FDA and others can take to respond to the opioid epidemic, with a particular focus on informing FDA's development of a formal method for incorporating individual and societal considerations into its risk-benefit framework for opioid approval and monitoring. |
| ab test results analysis: Analysis of Stresses in Bellows: Design criteria and test results W. F. Anderson, 1964 |
| ab test results analysis: Bayesian Methods for Hackers Cameron Davidson-Pilon, 2015-09-30 Master Bayesian Inference through Practical Examples and Computation–Without Advanced Mathematical Analysis Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice–freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback. You’ll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing. Once you’ve mastered these techniques, you’ll constantly turn to this guide for the working PyMC code you need to jumpstart future projects. Coverage includes • Learning the Bayesian “state of mind” and its practical implications • Understanding how computers perform Bayesian inference • Using the PyMC Python library to program Bayesian analyses • Building and debugging models with PyMC • Testing your model’s “goodness of fit” • Opening the “black box” of the Markov Chain Monte Carlo algorithm to see how and why it works • Leveraging the power of the “Law of Large Numbers” • Mastering key concepts, such as clustering, convergence, autocorrelation, and thinning • Using loss functions to measure an estimate’s weaknesses based on your goals and desired outcomes • Selecting appropriate priors and understanding how their influence changes with dataset size • Overcoming the “exploration versus exploitation” dilemma: deciding when “pretty good” is good enough • Using Bayesian inference to improve A/B testing • Solving data science problems when only small amounts of data are available Cameron Davidson-Pilon has worked in many areas of applied mathematics, from the evolutionary dynamics of genes and diseases to stochastic modeling of financial prices. His contributions to the open source community include lifelines, an implementation of survival analysis in Python. Educated at the University of Waterloo and at the Independent University of Moscow, he currently works with the online commerce leader Shopify. |
| ab test results analysis: Drug-Induced Liver Injury , 2019-07-13 Drug-Induced Liver Injury, Volume 85, the newest volume in the Advances in Pharmacology series, presents a variety of chapters from the best authors in the field. Chapters in this new release include Cell death mechanisms in DILI, Mitochondria in DILI, Primary hepatocytes and their cultures for the testing of drug-induced liver injury, MetaHeps an alternate approach to identify IDILI, Autophagy and DILI, Biomarkers and DILI, Regeneration and DILI, Drug-induced liver injury in obesity and nonalcoholic fatty liver disease, Mechanisms of Idiosyncratic Drug-Induced Liver Injury, the Evaluation and Treatment of Acetaminophen Toxicity, and much more. - Includes the authority and expertise of leading contributors in pharmacology - Presents the latest release in the Advances in Pharmacology series |
| ab test results analysis: Too Big to Ignore Phil Simon, 2013-03-05 Residents in Boston, Massachusetts are automatically reporting potholes and road hazards via their smartphones. Progressive Insurance tracks real-time customer driving patterns and uses that information to offer rates truly commensurate with individual safety. Google accurately predicts local flu outbreaks based upon thousands of user search queries. Amazon provides remarkably insightful, relevant, and timely product recommendations to its hundreds of millions of customers. Quantcast lets companies target precise audiences and key demographics throughout the Web. NASA runs contests via gamification site TopCoder, awarding prizes to those with the most innovative and cost-effective solutions to its problems. Explorys offers penetrating and previously unknown insights into healthcare behavior. How do these organizations and municipalities do it? Technology is certainly a big part, but in each case the answer lies deeper than that. Individuals at these organizations have realized that they don't have to be Nate Silver to reap massive benefits from today's new and emerging types of data. And each of these organizations has embraced Big Data, allowing them to make astute and otherwise impossible observations, actions, and predictions. It's time to start thinking big. In Too Big to Ignore, recognized technology expert and award-winning author Phil Simon explores an unassailably important trend: Big Data, the massive amounts, new types, and multifaceted sources of information streaming at us faster than ever. Never before have we seen data with the volume, velocity, and variety of today. Big Data is no temporary blip of fad. In fact, it is only going to intensify in the coming years, and its ramifications for the future of business are impossible to overstate. Too Big to Ignore explains why Big Data is a big deal. Simon provides commonsense, jargon-free advice for people and organizations looking to understand and leverage Big Data. Rife with case studies, examples, analysis, and quotes from real-world Big Data practitioners, the book is required reading for chief executives, company owners, industry leaders, and business professionals. |
| ab test results analysis: The Self-Service Data Roadmap Sandeep Uttamchandani, 2020-09-10 Data-driven insights are a key competitive advantage for any industry today, but deriving insights from raw data can still take days or weeks. Most organizations can’t scale data science teams fast enough to keep up with the growing amounts of data to transform. What’s the answer? Self-service data. With this practical book, data engineers, data scientists, and team managers will learn how to build a self-service data science platform that helps anyone in your organization extract insights from data. Sandeep Uttamchandani provides a scorecard to track and address bottlenecks that slow down time to insight across data discovery, transformation, processing, and production. This book bridges the gap between data scientists bottlenecked by engineering realities and data engineers unclear about ways to make self-service work. Build a self-service portal to support data discovery, quality, lineage, and governance Select the best approach for each self-service capability using open source cloud technologies Tailor self-service for the people, processes, and technology maturity of your data platform Implement capabilities to democratize data and reduce time to insight Scale your self-service portal to support a large number of users within your organization |
| ab test results analysis: Scientific Advertising Claude C. Hopkins, 1968 |
| ab test results analysis: Asymptotic Theory of Statistical Tests and Estimation Indra Mohan Chakravarti, 1980 Some memorable incidentes in probabilistic/statistica studies; Large deviation, tests, and estimates; Applications of characteristic function in solving some distribution problems; A chernoff-savage theorem for correlation ranl statistics with applications to sequential testing; Wiener - levy models, spherically exchangeable time series, and simultaneous inference in growth curve analysis; A note to the chung - erdors - sirao theorem; Asymptotic separation of distribution and convergence properties of tests and estimators; Density estimation: are theoretical results useful in practice? Stability theorems for characterizations of the normal and of the degenerate distribution; Estimation of the support contour-line of a probability law: limit law; Some estimation problems for the compound poisson distribution; A decomposition of infinite order and extreme multivariate distributions; Correction terms for multinomial large deviations; On a theorem of hoeffding; Sequential minimum probability ratio tests. |
| ab test results analysis: Discussions in User Experience Dave Lull, 2017-11-17 Understand the work of a modern UX professional and why UX is necessary for your business. Collated through years of online talks and work experience, this short collection of paraphrased discussions reveals the underlying psychology and philosophy of user experience decision making. Go beyond the rules to understand why the rules are there. Designed for anyone in business whose work is touching on UX – from developers to hiring managers - the topics in this book supersede the current thinking established in the IT world and touches on topics not often considered in UX education or in the workplace. Each discussion provides a launchpad for your own thinking and understanding. Written by an author with over 20 years’ experience in the field of UX, this book will show you how UX is not just about users, it’s about user welfare. What You'll Learn: Understand the psychology and philosophy of UX and why it is important Examine the underlying reasons behind many concepts, methods and tools Ensure the entire business offers a better experience to their users. Who this Book Is For Anyone who wants to make a career of UX design and/or architecture, including management. |
| ab test results analysis: Benchmarking the User Experience Jeff Sauro, 2018-06-25 This is a practical book about how to measure the user experience of websites, software, mobile apps, products, or just anything people use. This book is for UX researchers, designers, product owners, or anyone that has a vested interest in improving experience of websites and products--Introduction. |
| ab test results analysis: Statistical Methods for Machine Learning Jason Brownlee, 2018-05-30 Statistics is a pillar of machine learning. You cannot develop a deep understanding and application of machine learning without it. Cut through the equations, Greek letters, and confusion, and discover the topics in statistics that you need to know. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover the importance of statistical methods to machine learning, summary stats, hypothesis testing, nonparametric stats, resampling methods, and much more. |
| ab test results analysis: Encyclopedia of Machine Learning Claude Sammut, Geoffrey I. Webb, 2011-03-28 This comprehensive encyclopedia, in A-Z format, provides easy access to relevant information for those seeking entry into any aspect within the broad field of Machine Learning. Most of the entries in this preeminent work include useful literature references. |
| ab test results analysis: Handbook of Research Methods on Creativity Viktor Dörfler, Marc Stierand, 2020-07-31 This Handbook offers an insightful journey through the landscape of research methods used to study the phenomenon of creativity. Offering a methodological panorama for the global community of creativity researchers, contributors provide markers and waypoints to better orient scholars and encourage reflection on how one might produce exceptional research on the burgeoning field of creativity. |
| ab test results analysis: The Design of Experiments Sir Ronald Aylmer Fisher, 1974 |
| ab test results analysis: The Shock and Vibration Bulletin , 1982 |
| ab test results analysis: HBR Guide to Making Better Decisions Harvard Business Review, 2020-02-11 Learn how to make better; faster decisions. You make decisions every day--from prioritizing your to-do list to choosing which long-term innovation projects to pursue. But most decisions don't have a clear-cut answer, and assessing the alternatives and the risks involved can be overwhelming. You need a smarter approach to making the best choice possible. The HBR Guide to Making Better Decisions provides practical tips and advice to help you generate more-creative ideas, evaluate your alternatives fairly, and make the final call with confidence. You'll learn how to: Overcome the cognitive biases that can skew your thinking Look at problems in new ways Manage the trade-offs between options Balance data with your own judgment React appropriately when you've made a bad choice Communicate your decision--and overcome any resistance Arm yourself with the advice you need to succeed on the job, from a source you trust. Packed with how-to essentials from leading experts, the HBR Guides provide smart answers to your most pressing work challenges. |
| ab test results analysis: Experimentation Works Stefan H. Thomke, 2020-02-18 Don't fly blind. See how the power of experiments works for you. When it comes to improving customer experiences, trying out new business models, or developing new products, even the most experienced managers often get it wrong. They discover that intuition, experience, and big data alone don't work. What does? Running disciplined business experiments. And what if companies roll out new products or introduce new customer experiences without running these experiments? They fly blind. That's what Harvard Business School professor Stefan Thomke shows in this rigorously researched and eye-opening book. It guides you through best practices in business experimentation, illustrates how these practices work at leading companies, and answers some fundamental questions: What makes a good experiment? How do you test in online and brick-and-mortar businesses? In B2B and B2C? How do you build an experimentation culture? Also, best practice means running many experiments. Indeed, some hugely successful companies, such as Amazon, Booking.com, and Microsoft, run tens of thousands of controlled experiments annually, engaging millions of users. Thomke shows us how these and many other organizations prove that experimentation provides significant competitive advantage. How can managers create this capability at their own companies? Essential is developing an experimentation organization that prizes the science of testing and puts the discipline of experimentation at the center of its innovation process. While it once took companies years to develop the tools for such large-scale experiments, advances in technology have put these tools at the fingertips of almost any business professional. By combining the power of software and the rigor of controlled experiments, today's managers can make better decisions, create magical customer experiences, and generate big financial returns. Experimentation Works is your guidebook to a truly new way of thinking and innovating. |
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