This book introduces A/B testing in clear, practical terms for adult readers who want to understand how online experiments drive better decisions in business, product design, and everyday problem solving. Using straightforward examples, it explains how small, controlled changes can answer real questions-such as which headline performs better, which layout increases sign-ups, or which feature improves retention. The focus is on building sound judgment, data literacy, and evidence-based decision making. You'll learn how real organizations use experiments to reduce guesswork and measure impact. A famous large-scale web experiment once tested dozens of shades of blue and showed that tiny design changes can significantly affect user behavior-an example that illustrates why careful measurement matters and why intuition alone often fails. Rather than presenting A/B testing as a perfect process, the book emphasizes the moments where learning happens: failures, setbacks, and course corrections. Each chapter highlights what didn't work, what the experiment revealed, and how teams adjusted their approach-showing how experiments turn mistakes into useful information. Chapters walk you through forming testable hypotheses, choosing clear success metrics, setting up fair comparisons, and interpreting results without overclaiming. You'll also learn plain-language explanations of sample size, statistical noise, and common pitfalls. The book includes short exercises and prompts that help you practice designing experiments, tracking outcomes, and communicating findings. It also covers basic ethical safeguards-how to avoid harming user experience, monitor unintended effects, and stop tests that create problems-so experimentation remains responsible as well as effective.
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Paperback. Etat : new. Paperback. Unlock the potential of artificial intelligence in healthcare with this comprehensive beginner-friendly guide. Designed for adults new to the field, this book introduces you to how AI is revolutionizing medical diagnosis, treatment, and research. You'll learn the basics of AI concepts, data management, and key tools like machine learning and natural language processing. Explore real-world applications such as medical imaging, genomics, and decision support systems, along with practical strategies for implementation. Gain confidence in understanding the current landscape and ethical considerations, empowering you to navigate the future of AI in healthcare.- Understand fundamental AI concepts and their impact on medicine- Learn about different types of healthcare data and how they're managed- Discover how AI aids in imaging, genomics, and clinical decision-making- Find out how AI tools are integrated into real-world healthcare settings- Get guidance on starting your own exploration or project in medical AIEmpowering beginners to grasp the essentials of AI's role in transforming healthcare. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9798295535512
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Paperback. Etat : new. Paperback. This book introduces A/B testing in clear, practical terms for adult readers who want to understand how online experiments drive better decisions in business, product design, and everyday problem solving. Using straightforward examples, it explains how small, controlled changes can answer real questions-such as which headline performs better, which layout increases sign-ups, or which feature improves retention. The focus is on building sound judgment, data literacy, and evidence-based decision making.You'll learn how real organizations use experiments to reduce guesswork and measure impact. A famous large-scale web experiment once tested dozens of shades of blue and showed that tiny design changes can significantly affect user behavior-an example that illustrates why careful measurement matters and why intuition alone often fails.Rather than presenting A/B testing as a perfect process, the book emphasizes the moments where learning happens: failures, setbacks, and course corrections. Each chapter highlights what didn't work, what the experiment revealed, and how teams adjusted their approach-showing how experiments turn mistakes into useful information.Chapters walk you through forming testable hypotheses, choosing clear success metrics, setting up fair comparisons, and interpreting results without overclaiming. You'll also learn plain-language explanations of sample size, statistical noise, and common pitfalls.The book includes short exercises and prompts that help you practice designing experiments, tracking outcomes, and communicating findings. It also covers basic ethical safeguards-how to avoid harming user experience, monitor unintended effects, and stop tests that create problems-so experimentation remains responsible as well as effective. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. N° de réf. du vendeur 9798295535512
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