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⇱ Run Inference & Hypothesis Tests | Coursera

Run Inference & Hypothesis Tests

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Run Inference & Hypothesis Tests

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

3 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

3 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Statistical significance doesn’t always mean business impact; evaluate effect size alongside p-values.

  • Experiment design requires balancing Type I and Type II errors based on business risk and cost.

  • Statistical results must be translated into clear, actionable business recommendations.

Details to know

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Recently updated!

March 2026

Assessments

5 assignments¹

AI Graded see disclaimer
Taught in English

Build your subject-matter expertise

This course is part of the Statistical Inference & Predictive Modeling Foundations 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

There are 3 modules in this course

Statistics isn't just about numbers—it's about making confident decisions that drive business success.

This Short Course was created to help data analysts accomplish statistical inference with precision and clarity. By completing this course, you'll be able to apply confidence intervals to compare conversion rates across segments, evaluate error trade-offs in experimental design, conduct hypothesis tests in Python/R, and visualize power analysis to guide decision-making. By the end of this course, you will be able to: Apply statistical rigor to business problems with measurable impact Transform raw data into statistically-backed insights Communicate findings that stakeholders trust and act upon This course is unique because it bridges statistical theory with real-world application, using authentic business scenarios from leading tech companies. To be successful in this project, you should have a background in basic statistics and programming experience in Python or R.

Apply confidence-interval estimation to compare conversion rates across segments and present the statistical significance.

What's included

3 videos1 reading1 assignment1 ungraded lab

3 videos•Total 12 minutes
  • Why Statistical Confidence Matters in Business Decisions•2 minutes
  • Calculating Confidence Intervals for Conversion Rate Analysis•7 minutes
  • Building Confidence Intervals in Python for Segment Comparison•3 minutes
1 reading•Total 12 minutes
  • Foundations of Confidence Interval Theory and Application•12 minutes
1 assignment•Total 6 minutes
  • Confidence Interval Analysis Assessment•6 minutes
1 ungraded lab•Total 18 minutes
  • Segment Performance Analysis with Statistical Confidence•18 minutes

Evaluate Type I/II error trade-offs for a proposed test and recommend appropriate alpha and beta thresholds.

What's included

2 videos2 readings2 assignments

2 videos•Total 11 minutes
  • Calculating Optimal Alpha and Beta Thresholds•7 minutes
  • Implementing Error Analysis Framework in Python•4 minutes
2 readings•Total 16 minutes
  • Understanding Type I and Type II Errors in Business Context•10 minutes
  • Podcast: Navigating Error Trade-offs in Real-World Business Scenarios•6 minutes
2 assignments•Total 26 minutes
  • Strategic Error Management for Business Testing•18 minutes
  • Error Trade-off Analysis Assessment•8 minutes

Conduct a two-sample t-test in Python/R, interpret p-values, translate outcomes into plain-language business recommendations, and analyze test power under varying sample sizes.

What's included

3 videos1 reading2 assignments1 ungraded lab

3 videos•Total 13 minutes
  • Why Statistical Rigor Drives Business Success•2 minutes
  • Implementing Two-Sample t-Tests for Business Decisions•7 minutes
  • Building Complete Statistical Analysis in Python•3 minutes
1 reading•Total 11 minutes
  • Foundations of Two-Sample t-Tests for Business Analysis•11 minutes
2 assignments•Total 21 minutes
  • Course-Level Statistical Testing and Analysis Assessment•15 minutes
  • Two-Sample t-Tests & Power Analysis Knowledge Check•6 minutes
1 ungraded lab•Total 17 minutes
  • Complete Statistical Analysis with Power Optimization•17 minutes

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Frequently asked questions

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

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¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.