Intro to Inferential Statistics (Udacity)

Offered by Udacity,
Intro to Inferential Statistics (Udacity)

Making Predictions from Data. Inferential statistics allows us to draw conclusions from data that might not be immediately obvious. This course focuses on enhancing your ability to develop hypotheses and use common tests such as t-tests, ANOVA tests, and regression to validate your claims.

Class Deals by MOOC List - Click here and see Udacity's Active Discounts, Deals, and Promo Codes.

What You Will Learn

Lesson 1
Estimation

  • Estimate population parameters from sample statistics using confidence intervals.
  • Estimate the effect of a treatment.

Lesson 2
Hypothesis Testing

  • How to determine if a treatment has changed the value of a population parameter.

Lesson 3
t-tests

  • How to test the effect of a treatment.
  • Compare the difference in means for two groups when there are small sample sizes.

Lesson 4
ANOVA

  • Learn how to test whether or not there are differences between three or more groups.

Lesson 5
Correlation

  • Learn how to describe and test the strength of a relationship between two variables.

Lesson 6
Regression

  • How changes in one variable are related to changes in a second variable.

Lesson 7
Chi-squared Tests

  • Learn how to compare and test frequencies for categorical data.

Prerequisites and Requirements
This course assumes basic understanding of Descriptive Statistics, specifically the following:

  • calculating the mean and standard deviation of a data set
  • central limit theorem
  • interpreting probability and probability distributions
  • normal distributions and sampling distributions
  • normalizing observations

If you need a refresher, check out our [Descriptive Statistics course!]() The course also utilizes Google Spreadsheets as a tool.

Why Take This Course
This course will guide you through some of the basic tools of inferential statistics.
This course will cover:

  • estimating parameters of a population using sample statistics
  • hypothesis testing and confidence intervals
  • t-tests and ANOVA
  • correlation and regression
  • chi-squared test
Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Managing Data Analysis (Coursera) Coursera
Johns Hopkins University

Managing Data Analysis (Coursera)

This one-week course describes the process of analyzing data and how to manage that process. We describe the iterative nature of data analysis and the role of stating a sharp question, exploratory data analysis, inference, formal statistical modeling, interpretation, and communication. In addition, we will describe how to direct analytic activities within a team and to drive the data analysis process towards coherent and useful results.

Jul 27th 2026
1 Week
Business Applications of Hypothesis Testing and Confidence Interval Estimation (Coursera) Coursera
Rice University

Business Applications of Hypothesis Testing and Confidence Interval Estimation (Coursera)

Confidence intervals and Hypothesis tests are very important tools in the Business Statistics toolbox. A mastery over these topics will help enhance your business decision making and allow you to understand and measure the extent of ‘risk’ or ‘uncertainty’ in various business processes. This course advances your knowledge about Business Statistics by introducing you to Confidence Intervals and Hypothesis Testing. These are done by easy to understand applications.

Aug 10th 2026
4 Weeks
Statistics (Udacity) Udacity
Udacity,San Jose State University

Statistics (Udacity)

The Science of Decisions. We live in a time of unprecedented access to information...data. Whether researching the best school, job, or relationship, the Internet has thrown open the doors to vast pools of data. Statistics are simply objective and systematic methods for describing and interpreting information so that you may make the most informed decisions about life.

Self Paced
Self-Paced
Introduction to Machine Learning Course (Udacity) Udacity
Udacity

Introduction to Machine Learning Course (Udacity)

This class will teach you the end-to-end process of investigating data through a machine learning lens. Learn online, with Udacity. Machine Learning is a first-class ticket to the most exciting careers in data analysis today. As data sources proliferate along with the computing power to process them, going straight to the data is one of the most straightforward ways to quickly gain insights and make predictions.

Self Paced
Self-Paced
Statistics and Data Analysis with Excel, Part 1 (Coursera) Coursera
University of Colorado Boulder

Statistics and Data Analysis with Excel, Part 1 (Coursera)

Designed for students with no prior statistics knowledge, this course will provide a foundation for further study in data science, data analytics, or machine learning. Topics include descriptive statistics, probability, and discrete and continuous probability distributions. Assignments are conducted in Microsoft Excel (Windows or Mac versions). Designed to be taken with the follow-up course, “Statistics and Data Analysis with Excel, Part 2.”

Aug 3rd 2026
5-12 Weeks
Bayesian Statistics: Techniques and Models (Coursera) Coursera
University of California, Santa Cruz

Bayesian Statistics: Techniques and Models (Coursera)

This is the second of a two-course sequence introducing the fundamentals of Bayesian statistics. It builds on the course Bayesian Statistics: From Concept to Data Analysis, which introduces Bayesian methods through use of simple conjugate models. Real-world data often require more sophisticated models to reach realistic conclusions. This course aims to expand our “Bayesian toolbox” with more general models, and computational techniques to fit them.

Aug 10th 2026
5-12 Weeks
Statistical Inference and Hypothesis Testing in Data Science Applications (Coursera) Coursera
University of Colorado Boulder

Statistical Inference and Hypothesis Testing in Data Science Applications (Coursera)

This course will focus on theory and implementation of hypothesis testing, especially as it relates to applications in data science. Students will learn to use hypothesis tests to make informed decisions from data. Special attention will be given to the general logic of hypothesis testing, error and error rates, power, simulation, and the correct computation and interpretation of p-values. Attention will also be given to the misuse of testing concepts, especially p-values, and the ethical implications of such misuse.

Aug 10th 2026
5-12 Weeks
Strategic Planning and Execution (Coursera) Coursera
University of Virginia

Strategic Planning and Execution (Coursera)

Avoid the pitfalls of strategy planning and execution with the tools and skills from this course. You'll learn the pillars of strategy execution--analysis, formulation, and implementation--and how to use the 4A model to effectively approach strategy execution. Finally, a panel of leaders from entrepreneurs, nonprofits, and industry, share their expertise gleaned from years of successful strategy planning and execution.

Jul 27th 2026
4 Weeks
Data Analysis and Visualization (Udacity) Udacity
Georgia Institute of Technology,Udacity

Data Analysis and Visualization (Udacity)

Data and visual analytics is an emerging field concerned with analyzing, modeling, and visualizing complex high dimensional data. This course will introduce students to the field by covering state­-of-­the-art modeling, analysis and visualization techniques. It will emphasize practical challenges involving complex real world data and include several case studies and hands-on work with the R programming language.

Self Paced
Self-Paced
A Crash Course in Data Science (Coursera) Coursera
Johns Hopkins University

A Crash Course in Data Science (Coursera)

By now you have definitely heard about data science and big data. In this one-week class, we will provide a crash course in what these terms mean and how they play a role in successful organizations. This class is for anyone who wants to learn what all the data science action is about, including those who will eventually need to manage data scientists. The goal is to get you up to speed as quickly as possible on data science without all the fluff. We've designed this course to be as convenient as possible without sacrificing any of the essentials.

Jul 27th 2026
1 Week