University of Colorado Boulder
Statistical Inference and Hypothesis Testing in Data Science Applications
University of Colorado Boulder

Statistical Inference and Hypothesis Testing in Data Science Applications

This course is part of Data Science Foundations: Statistical Inference Specialization

Taught in English

Some content may not be translated

Jem Corcoran

Instructor: Jem Corcoran

5,100 already enrolled

Included with Coursera Plus

Course

Gain insight into a topic and learn the fundamentals

4.7

(37 reviews)

Intermediate level

Recommended experience

38 hours (approximately)
Flexible schedule
Learn at your own pace
Progress towards a degree

What you'll learn

  • Define a composite hypothesis and the level of significance for a test with a composite null hypothesis.

  • Define a test statistic, level of significance, and the rejection region for a hypothesis test. Give the form of a rejection region.

  • Perform tests concerning a true population variance.

  • Compute the sampling distributions for the sample mean and sample minimum of the exponential distribution.

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

6 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.7

(37 reviews)

Intermediate level

Recommended experience

38 hours (approximately)
Flexible schedule
Learn at your own pace
Progress towards a degree

See how employees at top companies are mastering in-demand skills

Placeholder

Build your subject-matter expertise

This course is part of the Data Science Foundations: Statistical Inference Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate
Placeholder
Placeholder

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV

Share it on social media and in your performance review

Placeholder

There are 6 modules in this course

Welcome to the course! This module contains logistical information to get you started!

What's included

6 readings1 app item1 discussion prompt1 ungraded lab

In this module, we will define a hypothesis test and develop the intuition behind designing a test. We will learn the language of hypothesis testing, which includes definitions of a null hypothesis, an alternative hypothesis, and the level of significance of a test. We will walk through a very simple test.

What's included

6 videos12 readings1 quiz1 programming assignment2 ungraded labs

In this module, we will expand the lessons of Module 1 to composite hypotheses for both one and two-tailed tests. We will define the “power function” for a test and discuss its interpretation and how it can lead to the idea of a “uniformly most powerful” test. We will discuss and interpret “p-values” as an alternate approach to hypothesis testing.

What's included

7 videos8 readings1 quiz1 programming assignment1 ungraded lab

In this module, we will learn about the chi-squared and t distributions and their relationships to sampling distributions. We will learn to identify when hypothesis tests based on these distributions are appropriate. We will review the concept of sample variance and derive the “t-test”. Additionally, we will derive our first two-sample test and apply it to make some decisions about real data.

What's included

7 videos8 readings1 quiz1 programming assignment1 ungraded lab

In this module, we will consider some problems where the assumption of an underlying normal distribution is not appropriate and will expand our ability to construct hypothesis tests for this case. We will define the concept of a “uniformly most powerful” (UMP) test, whether or not such a test exists for specific problems, and we will revisit some of our earlier tests from Modules 1 and 2 through the UMP lens. We will also introduce the F-distribution and its role in testing whether or not two population variances are equal.

What's included

6 videos7 readings2 quizzes

In this module, we develop a formal approach to hypothesis testing, based on a “likelihood ratio” that can be more generally applied than any of the tests we have discussed so far. We will pay special attention to the large sample properties of the likelihood ratio, especially Wilks’ Theorem, that will allow us to come up with approximate (but easy) tests when we have a large sample size. We will close the course with two chi-squared tests that can be used to test whether the distributional assumptions we have been making throughout this course are valid.

What's included

5 videos7 readings1 quiz1 programming assignment1 ungraded lab

Instructor

Instructor ratings
4.9 (12 ratings)
Jem Corcoran
University of Colorado Boulder
6 Courses24,930 learners

Offered by

Recommended if you're interested in Probability and Statistics

Why people choose Coursera for their career

Felipe M.
Learner since 2018
"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."
Jennifer J.
Learner since 2020
"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."
Larry W.
Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."
Chaitanya A.
"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

Learner reviews

Showing 3 of 37

4.7

37 reviews

  • 5 stars

    78.94%

  • 4 stars

    15.78%

  • 3 stars

    5.26%

  • 2 stars

    0%

  • 1 star

    0%

MM
4

Reviewed on Jul 6, 2023

PR
5

Reviewed on Jan 14, 2024

DP
5

Reviewed on Feb 8, 2024

New to Probability and Statistics? Start here.

Placeholder

Open new doors with Coursera Plus

Unlimited access to 7,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription

Advance your career with an online degree

Earn a degree from world-class universities - 100% online

Join over 3,400 global companies that choose Coursera for Business

Upskill your employees to excel in the digital economy

Frequently asked questions