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Java for Data Science

You're reading from  Java for Data Science

Product type Book
Published in Jan 2017
Publisher Packt
ISBN-13 9781785280115
Pages 386 pages
Edition 1st Edition
Languages
Authors (2):
Richard M. Reese Richard M. Reese
Profile icon Richard M. Reese
Jennifer L. Reese Jennifer L. Reese
Profile icon Jennifer L. Reese
View More author details

Table of Contents (19) Chapters

Java for Data Science
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
1. Getting Started with Data Science 2. Data Acquisition 3. Data Cleaning 4. Data Visualization 5. Statistical Data Analysis Techniques 6. Machine Learning 7. Neural Networks 8. Deep Learning 9. Text Analysis 10. Visual and Audio Analysis 11. Mathematical and Parallel Techniques for Data Analysis 12. Bringing It All Together

Hypothesis testing


Hypothesis testing is used to test whether certain assumptions, or premises, about a dataset could not happen by chance. If this is the case, then the results of the test are considered to be statistically significant.

Performing hypothesis testing is not a simple task. There are many different pitfalls to avoid such as the placebo effect or the observer effect. In the former, a participant will attain a result that they think is expected. In the observer effect, also called the Hawthorne effect, the results are skewed because the participants know they are being watched. Due to the complex nature of human behavior analysis, some types of statistical analysis are particularly subject to skewing or corruption.

The specific methods for performing hypothesis testing are outside the scope of this book and require a solid background in statistical processes and best practices. Apache Commons provides a package, org.apache.commons.math3.stat.inference, with tools for performing...

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