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Improving your statistical inferences

Approx. 28 hours to complete

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Course Summary

Learn how to make statistical inferences that help you make decisions based on data in this course. You will explore the concepts of probability, hypothesis testing, and confidence intervals through real-world examples.

Key Learning Points

Understand the basics of probability theory and its applications
Learn how to conduct hypothesis testing and interpret the results
Explore confidence intervals and how they can be used to make informed decisions

Job Positions & Salaries of people who have taken this course might have

Data Analyst
- USA: $65,000 - $110,000
- India: INR 350,000 - INR 1,100,000
- Spain: €25,000 - €45,000
Market Research Analyst
- USA: $45,000 - $90,000
- India: INR 300,000 - INR 900,000
- Spain: €20,000 - €35,000
Quantitative Analyst
- USA: $70,000 - $150,000
- India: INR 500,000 - INR 2,000,000
- Spain: €40,000 - €70,000

Learning Outcomes

Understand the fundamentals of probability theory
Be able to conduct hypothesis testing and interpret results
Use confidence intervals to make informed decisions

Prerequisites or good to have knowledge before taking this course

Basic knowledge of algebra
Familiarity with Excel or another spreadsheet program

Course Difficulty Level

Intermediate

Course Format

Self-paced
Online
Video lectures

Similar Courses

Introduction to Probability and Data
Bayesian Statistics: From Concept to Data Analysis
Inferential Statistics

Related Education Paths

Notable People in This Field

Nate Silver
Andrew Gelman
Hadley Wickham

Related Books

Description

This course aims to help you to draw better statistical inferences from empirical research. First, we will discuss how to correctly interpret p-values, effect sizes, confidence intervals, Bayes Factors, and likelihood ratios, and how these statistics answer different questions you might be interested in. Then, you will learn how to design experiments where the false positive rate is controlled, and how to decide upon the sample size for your study, for example in order to achieve high statistical power. Subsequently, you will learn how to interpret evidence in the scientific literature given widespread publication bias, for example by learning about p-curve analysis. Finally, we will talk about how to do philosophy of science, theory construction, and cumulative science, including how to perform replication studies, why and how to pre-register your experiment, and how to share your results following Open Science principles.

In practical, hands on assignments, you will learn how to simulate t-tests to learn which p-values you can expect, calculate likelihood ratio's and get an introduction the binomial Bayesian statistics, and learn about the positive predictive value which expresses the probability published research findings are true. We will experience the problems with optional stopping and learn how to prevent these problems by using sequential analyses. You will calculate effect sizes, see how confidence intervals work through simulations, and practice doing a-priori power analyses. Finally, you will learn how to examine whether the null hypothesis is true using equivalence testing and Bayesian statistics, and how to pre-register a study, and share your data on the Open Science Framework. All videos now have Chinese subtitles. More than 30.000 learners have enrolled so far! If you enjoyed this course, I can recommend following it up with me new course "Improving Your Statistical Questions"

Outline

Introduction + Frequentist Statistics
Introduction
Frequentism, Likelihoods, Bayesian statistics
What is a p-value
Type 1 and Type 2 errors
Structure of the Course
Passing the Course
Research on Quizzes
Week 1: Overview
Assignment 1: Which p-values can you expect?
Consent Form for Use of Data
Pop Quiz!
Answer Form Assignment 1 : Which p-values can you expect?
Pop Quiz 2!
Exam Week 1

Likelihoods & Bayesian Statistics
Interview: Zoltan Dienes
Likelihoods
Binomial Bayesian Inference
Bayesian Thinking
Week 2: Overview
Interview with Professor Zoltan Dienes
Assignment 2.1: Likelihoods
Assignment 2.2: Bayesian Statistics
Answer Form Assignment 2.1
Answer Form Assignment 2.2: Bayesian Statistics
Pop Quiz 3!
Exam Week 2

Multiple Comparisons, Statistical Power, Pre-Registration
Type 1 error control
Type 2 error control
Interview Professor Dan Simons
Pre-registration
Week 3: Overview
Assignment 3.1: Positive Predictive Value
Assignment 3.2: Optional Stopping
Interview Professor Dan Simons
Answer Form Assignment 3.1: Positive Predictive Value
Answer Form Assignment 3.2: Optional Stopping
Exam Week 3

Effect Sizes
Effect Sizes
Cohen's d
Correlations
Week 4: Overview
Assignment 4: Calculating Effect Sizes
Answer Form Assignment 4: Effect Sizes
Pop Quiz 4!
Exam Week 4

Confidence Intervals, Sample Size Justification, P-Curve analysis
Confidence Intervals
Sample Size Justification
P-Curve Analysis
Week 5: Overview
Assignment 5.1: Confidence Intervals
Assignment 5.2: Random Variation and Power Analysis
Answer Form Assignment 5.1: Confidence Intervals and Capture Percentages
Answer Form Assignment 5.2: Random Variation and Power Analysis
Pop Quiz 5!
Exam Week 5

Philosophy of Science & Theory
Philosophy of Science
The Null is Always False
Theory Construction
Week 6: Overview
Assignment 6: Equivalence Testing
Answer Form Assignment 6: Equivalence Testing
Exam Week 6

Open Science
Replications
Publication Bias
Open Science
Week 7: Overview

Final Exam
Pop Quiz 6!
Practice Exam
Graded Final Exam

Summary of User Reviews

Learn Statistical Inferences on Coursera - Read reviews from verified students who took this course. Find out what they think of the course and what they learned.

Key Aspect Users Liked About This Course

The course provides a comprehensive understanding of statistical inferences.

Pros from User Reviews

Great content that is easy to understand
Well-structured course with clear explanations
Engaging lectures that keep you interested
Access to helpful resources and support
The course is taught by highly qualified instructors

Cons from User Reviews

Some students found the pace of the course to be too fast
The course requires a significant time commitment
The course can be challenging for those without a strong math background
Some students reported technical difficulties with the platform
The course can be expensive for those on a tight budget

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