Übersicht			Auf mobile öffnen

Lieferstatus:	i.d.R. innert 7-14 Tagen versandfertig
Veröffentlichung:	September 2025
Genre:	EDV / Informatik
ISBN:	9798295535208
EAN-Code:	9798295535208
Verlag:	Little Big Giant
Einband:	Kartoniert
Sprache:	English
Dimensionen:	H 191 mm / B 140 mm / D 12 mm
Gewicht:	242 gr
Seiten:	212
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

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.