This introductory textbook takes a building-block approach that emphasizes the application and interpretation of statistics in research in crime and justice. This text is meant for both students and professionals who want to gain a basic understanding of common statistical methods used in criminology and criminal justice before advancing to more complex statistical analyses in future volumes. 

This book emphasizes comprehension and interpretation. As the statistical methods discussed become more complex and demanding to compute, it integrates statistical software. It provides readers with an accessible understanding of popular statistical programs used to examine real-life crime and justice problems (including SPSS, Stata, and R). In addition, the book includes supplemental resources such as a glossary of key terms, practice questions, and sample data.

Basic Statistics in Criminology and Criminal Justice aims to give students and researchers a core understanding of statistical concepts and methods that will leave them with the confidence and tools to tackle the statistical problems in their own research work.

Introduction: Statistics as a Research Tool

Measurement: the Basic Building Block of Research
Representing and Displaying Data
Describing the Typical Case: Measures of Central Tendency
How Typical is the Typical Case?: Measuring Dispersion
The Logic of Statistical Inference: Making Statements About Populations from Sample Statistics
Defining the Observed Significance Level of a Test: A simple Example Using the Binomial Distribution
Steps in a Statistical Test: Using the Binomial Distribution to Make Decisions About Hypotheses
Chi-Square: A Test Commonly Used for Nominal-Level Measures
The Normal Distribution and Its Application to Tests of Statistical Significance
Comparing Means and Proportions in Two Samples
Comparing Means Among More than Two Samples: Analysis of Variance
Measures of Association for Nominal and Ordinal Variables
Measuring Association for Interval-Level Data: Pearson's Correlation Coefficient
An Introduction to Bivariate Regression<p></p><p></p>