Includes bibliographical references (pages 477-481) and index.

About the authors --
Getting started --
The big picture --
Examining our data : an introduction to some of the techniques of exploratory data analysis --
The behavior of data --
Properties of distributions : the building blocks of statistical inference --
The basics of statistical inference : drawing conclusions from our data --
Estimating parameters of populations from sample data --
Resistant estimators of parameters --
General principles of hypothesis testing --
Specific techniques to answer specific questions --
The independent groups t-tests for testing for differences between population means --
Testing hypotheses where the dependent variable consists of frequencies of scores in various categories --
The randomization/permutation model : an alternative to the classical statistical model for testing hypotheses about treatment effects --
Exploring the relationship between two variables : correlation --
Exploring the relationship between two variables : the linear regression model --
A closer look at linear regression --
Another way to scale the size of treatment effects --
Analysis of variance for testing for differences between population means --
Multiple regression and beyond --
Epilogue --
Appendices --
A. Some useful rules of algebra --
B. Rules of summation --
C. Logarithms --
Table of the inverse of the cumulative normal distribution --
E. The unit normal distribution --
F. The t distribution --
G. Table for the fisher r to Zr transformation --
H. Critical values for F with alpha = .05 --
The chi square distribution --
Index.

Using real research on antisocial behaviours such as cyberbullying, prejudice and discrimination, this text helps readers across the social sciences to understand the underlying theory behind statistical methods.