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Intro to Inferential Statistics

Free Course

Making Predictions from Data

Related Nanodegree Program

Digital Marketing

About this course

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.

What you will learn

  1. Estimation
    • Estimate population parameters from sample statistics using confidence intervals.
    • Estimate the effect of a treatment.
  2. Hypothesis Testing
    • How to determine if a treatment has changed the value of a population parameter.
  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.
  4. ANOVA
    • Learn how to test whether or not there are differences between three or more groups.
  5. Correlation
    • Learn how to describe and test the strength of a relationship between two variables.
  6. Regression
    • How changes in one variable are related to changes in a second variable.
  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.

See the Technology Requirements for using Udacity.

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

Learn with the best.

  • Ronald Rogers
    Ronald Rogers


  • Katie Kormanik
    Katie Kormanik


  • Sean Laraway
    Sean Laraway