Our modern day, multimedia, information-obsessed culture has fundamentally altered much of what we do day-to-day. The way we shop and pay bills. The way we communicate. The way we research, study, and learn. In the realm of travel we have more tools than ever telling us where to go, how to get there, what it will look like, what to do, and why we should go in the first place. This proliferation of constantly updated data has changed the way we go about our journeys. But how? By tracing the evolution of the guidebook from pilgrim manuals and Baedeker’s books to Yelp reviews and Google Maps, David Bockino explores the effects this information growth has had on the state of travel and adventure. Inspired by some of the world’s greatest explorers, he sets out guidebook-less to a destination he knows little about, launching an experiment to determine just how the guidebook and its digital descendants have transformed the nature of travel. The Guidebook Experiment is a call-to-action to conduct our own guidebook experiments, to disconnect from the ceaseless barrage of information in modern life and explore an unknown neighborhood or unfamiliar country and discover the joy of travel on our own.

Covers experiment planning, execution, analysis, and reporting This single-source resource guides readers in planning and conducting credible experiments for engineering, science, industrial processes, agriculture, and business. The text takes experimenters all the way through conducting a high-impact experiment, from initial conception, through execution of the experiment, to a defensible final report. It prepares the reader to anticipate the choices faced during each stage. Filled with real-world examples from engineering science and industry, Planning and Executing Credible Experiments: A Guidebook for Engineering, Science, Industrial Processes, Agriculture, and Business offers chapters that challenge experimenters at each stage of planning and execution and emphasizes uncertainty analysis as a design tool in addition to its role for reporting results. Tested over decades at Stanford University and internationally, the text employs two powerful, free, open-source software tools: GOSSET to optimize experiment design, and R for statistical computing and graphics. A website accompanies the text, providing additional resources and software downloads. A comprehensive guide to experiment planning, execution, and analysis Leads from initial conception, through the experiment’s launch, to final report Prepares the reader to anticipate the choices faced throughout an experiment Hones the motivating question Employs principles and techniques from Design of Experiments (DoE) Selects experiment designs to obtain the most information from fewer experimental runs Offers chapters that propose questions that an experimenter will need to ask and answer during each stage of planning and execution Demonstrates how uncertainty analysis guides and strengthens each stage Includes examples from real-life industrial experiments Accompanied by a website hosting open-source software Planning and Executing Credible Experiments is an excellent resource for graduates and senior undergraduates—as well as professionals—across a wide variety of engineering disciplines.

This is a guidebook for those who want to use computational experiments to support their work in algorithm design and analysis. Numerous case studies and examples show how to apply these concepts. All the necessary concepts in computer architecture and data analysis are covered so that the book can be used by anyone who has taken a course or two in data structures and algorithms.

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.

Getting numbers is easy; getting numbers you can trust is hard. This practical guide by experimentation leaders at Google, LinkedIn, and Microsoft will teach you how to accelerate innovation using trustworthy online controlled experiments, or A/B tests. Based on practical experiences at companies that each run more than 20,000 controlled experiments a year, the authors share examples, pitfalls, and advice for students and industry professionals getting started with experiments, plus deeper dives into advanced topics for practitioners who want to improve the way they make data-driven decisions. Learn how to • Use the scientific method to evaluate hypotheses using controlled experiments • Define key metrics and ideally an Overall Evaluation Criterion • Test for trustworthiness of the results and alert experimenters to violated assumptions • Build a scalable platform that lowers the marginal cost of experiments close to zero • Avoid pitfalls like carryover effects and Twyman's law • Understand how statistical issues play out in practice.

Perfect for middle- and high-school students and DIY enthusiasts, this full-color guide teaches you the basics of biology lab work and shows you how to set up a safe lab at home. Features more than 30 educational (and fun) experiments.

Like other sciences and engineering disciplines, software engineering requires a cycle of model building, experimentation, and learning. Experiments are valuable tools for all software engineers who are involved in evaluating and choosing between different methods, techniques, languages and tools. The purpose of Experimentation in Software Engineering is to introduce students, teachers, researchers, and practitioners to empirical studies in software engineering, using controlled experiments. The introduction to experimentation is provided through a process perspective, and the focus is on the steps that we have to go through to perform an experiment. The book is divided into three parts. The first part provides a background of theories and methods used in experimentation. Part II then devotes one chapter to each of the five experiment steps: scoping, planning, execution, analysis, and result presentation. Part III completes the presentation with two examples. Assignments and statistical material are provided in appendixes. Overall the book provides indispensable information regarding empirical studies in particular for experiments, but also for case studies, systematic literature reviews, and surveys. It is a revision of the authors’ book, which was published in 2000. In addition, substantial new material, e.g. concerning systematic literature reviews and case study research, is introduced. The book is self-contained and it is suitable as a course book in undergraduate or graduate studies where the need for empirical studies in software engineering is stressed. Exercises and assignments are included to combine the more theoretical material with practical aspects. Researchers will also benefit from the book, learning more about how to conduct empirical studies, and likewise practitioners may use it as a “cookbook” when evaluating new methods or techniques before implementing them in their organization.