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Intro to Inferential Statistics

San Jose State University via Udacity


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.

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


Inferential Statistics is a continuation of the material covered in Descriptive Statistics, and so lesson numbers follow from that course:

Lesson 8: Estimation

You will learn how to estimate population parameters from sample statistics using confidence intervals and estimating the effect of a treatment.

Lesson 9: Hypothesis Testing

You will learn how to use critical values to make decisions on whether or not a treatment has changed the value of a population parameter.

Lesson 10,11: t-tests

You will learn how to test the effect of a treatment or compare the difference in means for two groups when we have small sample sizes.

Lesson 12,13: ANOVA

You will learn how to test whether or not there are differences between three or more groups.

Lesson 14: Correlation

You will learn how to describe and test the strength of a relationship between two variables.

Lesson 15: Regression

You will learn how to describe the way in which changes in one variable are related to changes in a second variable.

Lesson 16: Chi-squared Tests

You will learn how to compare and test frequencies for categorical data.

Final Project

You will use the methods you have learned in this course to perform an analysis on a dataset and report your findings. You will describe the data, calculate statistics, perform inference, and make conclusions.

Taught by

Katie Kormanik, Ronald Rogers and Sean Laraway


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4.5 rating, based on 8 reviews

Start your review of Intro to Inferential Statistics

  • Pushkar Dk completed this course, spending 7 hours a week on it and found the course difficulty to be medium.

    A very good second course on elementary statistics. It requires us to finish 'descriptive statistics' before starting it and is a much lengthier course. However, the teaching style is very nice with emphasis on "why?" rather than simply calculating variables without understanding the concepts. The lecturers go to great lengths to help us visualize the data as well (I've come to believe that any course from Katie Kormanik must be easy to understand)!
  • Deepal D'silva completed this course, spending 4 hours a week on it and found the course difficulty to be medium.

    This is an excellent course for intermediate statistics. This course builds upon the Descriptive statistics course. The method of teaching focuses on understanding and applying various concepts rather than just plugging values in formulas.
  • Sonsoles López is taking this course right now, spending 2 hours a week on it and found the course difficulty to be very easy.

    Great course for getting started on Inferential Statistics. Very didactic. The teacher is great and all the concepts are very well explained
  • Peter Thwaites completed this course, spending 15 hours a week on it and found the course difficulty to be medium.

  • Mitch Nakaue completed this course.

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    Donghyun Kang

    Donghyun Kang completed this course.

  • Klaas Naaijkens

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  • Drishti Bansal is taking this course right now.

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