This article is a solid introduction to statistical testing, for beginners, as well as a reference for practitioners. It includes numerous examples as well as illustrations and definitions for concepts such as rejecting the null hypothesis, one sample hypothesis testing, P-values, critical values, and Bayesian hypothesis testing. It has references to additional topics, such as

- What is Ad Hoc Testing?
- What is a Rejection Region?
- What is a Two Tailed Test?
- How to Decide if a Hypothesis Test is a One Tailed Test or a Two Tailed Test.
- How to Decide if a Hypothesis is a Left Tailed Test or a Right-Tailed Test.
- How to State the Null Hypothesis in Statistics.
- How to Find a Critical Value.
- How to Support or Reject a Null Hypothesis.

*Picture: When to use ANOVA?*

You can read this tutorial here. For specific popular tests, check the following links below, from the same source:

- ANOVA.
- Chi Square Test for Normality
- Cochran-Mantel-Haenszel Test
- F Test
- Granger Causality Test.
- Hotelling’s T-Squared
- KPSS Test.
- What is a Likelihood-Ratio Test?
- Log rank test.
- MANCOVA
- Sequential Probability Ratio Test
- How to Run a Sign Test.
- T Test: one sample.
- T-Test: Two sample.
- Welch’s ANOVA.
- Welch’s Test for Unequal Variances.
- Z-Test: one sample.
- Z Test: Two Proportion
- Wald Test.

For non standard tests, follow this link.

© 2020 Data Science Central ® Powered by

Badges | Report an Issue | Privacy Policy | Terms of Service

**Most Popular Content on DSC**

To not miss this type of content in the future, subscribe to our newsletter.

- Book: Statistics -- New Foundations, Toolbox, and Machine Learning Recipes
- Book: Classification and Regression In a Weekend - With Python
- Book: Applied Stochastic Processes
- Long-range Correlations in Time Series: Modeling, Testing, Case Study
- How to Automatically Determine the Number of Clusters in your Data
- New Machine Learning Cheat Sheet | Old one
- Confidence Intervals Without Pain - With Resampling
- Advanced Machine Learning with Basic Excel
- New Perspectives on Statistical Distributions and Deep Learning
- Fascinating New Results in the Theory of Randomness
- Fast Combinatorial Feature Selection

**Other popular resources**

- Comprehensive Repository of Data Science and ML Resources
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- 100 Data Science Interview Questions and Answers
- Cheat Sheets | Curated Articles | Search | Jobs | Courses
- Post a Blog | Forum Questions | Books | Salaries | News

**Archives:** 2008-2014 |
2015-2016 |
2017-2019 |
Book 1 |
Book 2 |
More

**Most popular articles**

- Free Book and Resources for DSC Members
- New Perspectives on Statistical Distributions and Deep Learning
- Time series, Growth Modeling and Data Science Wizardy
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
- How to Automatically Determine the Number of Clusters in your Data
- Fascinating New Results in the Theory of Randomness
- Hire a Data Scientist | Search DSC | Find a Job
- Post a Blog | Forum Questions

## You need to be a member of Data Science Central to add comments!

Join Data Science Central