How do people search evidence for a hypothesis? A well documented answer in cognitive psychology is that they search for confirming evidence. However, the rational strategy is to try to falsify the hypothesis. This book critically evaluates this contradiction. Experimental research is discussed against the background of philosophical and formal theories of hypothesis testing with striking results: Falsificationism and verificationism - the two main rival philosophies of testing - come down to one and the same principle for concrete testing behaviour, eluding the contrast between rational falsification and confirmation bias. In this book, the author proposes a new perspective for describing hypothesis testing behaviour - the probability-value model - which unifies the contrasting views. According to this model, hypothesis testers pragmatically consider what evidence and how much evidence will convince them to reject or accept the hypothesis. They might either require highly probative evidence for its acceptance, at the risk of its rejection, or protect it against rejection and go for minor confirming observations. Interestingly, the model refines the classical opposition between rationality and pragmaticity because pragmatic considerations are a legitimate aspect of 'rational' hypothesis testing. Possible future research and applications of the ideas advanced are discussed, such as the modelling of expert hypothesis testing.

"Learning Statistics with R" covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software and adopting a light, conversational style throughout. The book discusses how to get started in R, and gives an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book. For more information (and the opportunity to check the book out before you buy!) visit http://ua.edu.au/ccs/teaching/lsr or http://learningstatisticswithr.com

Statistics for the Behavioral Sciences is an introduction to statistics text that will engage students in an ongoing spirit of discovery by illustrating how statistics apply to modern-day research problems. By integrating instructions, screenshots, and practical examples for using IBM SPSS® Statistics software, the book makes it easy for students to learn statistical concepts within each chapter. Gregory J. Privitera takes a user-friendly approach while balancing statistical theory, computation, and application with the technical instruction needed for students to succeed in the modern era of data collection, analysis, and statistical interpretation.

Printed in color. Introductory Business Statistics is designed to meet the scope and sequence requirements of the one-semester statistics course for business, economics, and related majors. Core statistical concepts and skills have been augmented with practical business examples, scenarios, and exercises. The result is a meaningful understanding of the discipline, which will serve students in their business careers and real-world experiences.

Designed to provide a variety of exercises that engage students actively in all phases of scientific investigation, from formulating research questions through interpreting and presenting final results. Suited to undergraduates, each chapter presents an animal behavior exercise tested by academic members of the Animal Behavior Society. Four types of exercises are presented: (1) traditional exercises in which students follow a pre-determined protocol to test particular hypotheses, (2) traditional exercises that can easily be adapted to inquiry-based approaches, (3) combined pedagogy exercises that involve both traditional and inquiry approaches, and (4) inquiry exercises in which students brainstorm to generate their own hypotheses, then design their own experiments to test them. Exercises cover descriptive ethology, causation and development of behavior, and behavioral ecology. Both field and laboratory exercises are included on arthropods, fish, amphibians, reptiles, birds, and mammals.

Statistics: A Short, Clear Guide is an accessible, humorous and easy introduction to statistics for social science students. In this refreshing book, experienced author and academic Neil Burdess shows that statistics are not the result of some mysterious "black magic", but rather the result of some very basic arithmetic. Getting rid of confusing x′s and y′s, he shows that it′s the intellectual questions that come before and after the calculations that are important: (i) What are the best statistics to use with your data? and (ii) What do the calculated statistics tell you? Statistics: A Short, Clear Guide aims to help students make sense of the logic of statistics and to decide how best to use statistics to analyse their own data. What′s more, it is not reliant on students having access to any particular kind of statistical software package. This is a very useful book for any student in the social sciences doing a statistics course or needing to do statistics for themselves for the first time.

Statistical Inference via Data Science: A ModernDive into R and the Tidyverse provides a pathway for learning about statistical inference using data science tools widely used in industry, academia, and government. It introduces the tidyverse suite of R packages, including the ggplot2 package for data visualization, and the dplyr package for data wrangling. After equipping readers with just enough of these data science tools to perform effective exploratory data analyses, the book covers traditional introductory statistics topics like confidence intervals, hypothesis testing, and multiple regression modeling, while focusing on visualization throughout. Features: ● Assumes minimal prerequisites, notably, no prior calculus nor coding experience ● Motivates theory using real-world data, including all domestic flights leaving New York City in 2013, the Gapminder project, and the data journalism website, FiveThirtyEight.com ● Centers on simulation-based approaches to statistical inference rather than mathematical formulas ● Uses the infer package for "tidy" and transparent statistical inference to construct confidence intervals and conduct hypothesis tests via the bootstrap and permutation methods ● Provides all code and output embedded directly in the text; also available in the online version at moderndive.com This book is intended for individuals who would like to simultaneously start developing their data science toolbox and start learning about the inferential and modeling tools used in much of modern-day research. The book can be used in methods and data science courses and first courses in statistics, at both the undergraduate and graduate levels.