Improving your statistical inferences from Coursera

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"

What's inside

Syllabus

Introduction + Frequentist Statistics

Likelihoods & Bayesian Statistics

Multiple Comparisons, Statistical Power, Pre-Registration

Effect Sizes

Confidence Intervals, Sample Size Justification, P-Curve analysis

Philosophy of Science & Theory

Open Science

Final Exam

This module contains a practice exam and a graded exam. Both quizzes cover content from the entire course. We recommend making these exams only after you went through all the other modules.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Covers a wide range of statistical concepts, including p-values, effect sizes, confidence intervals, and likelihood ratios

Taught by Daniel Lakens, a renowned expert in statistical inference

Provides hands-on assignments and simulations to reinforce learning

Emphasizes practical applications of statistical methods in research

Covers advanced topics such as sequential analyses, Open Science principles, and Bayesian statistics

May require a strong foundation in statistics for some learners

Reviews summary

Engaging statistics course with critical analysis

Learners say that Improving your statistical inferences is an engaging course that helps students critically evaluate statistical inferences. Instructor Daniel Lakens presents concepts in clear and interesting ways with a focus on helping learners become critical consumers of statistical information. Through hands-on assignments using R programming and open science, learners develop a practical understanding of statistical methods and interpretation. This course is highly recommended for anyone interested in statistics, research, or data analysis.

Appropriate for learners with varying backgrounds in statistics

"I am so glad that i enrolled and completed this course. It is an excellently designed course and offer us an understanding on how critical decisions on inferential statistics are, which is definitely not taught in universities."

"This eight week course is well presented, with plenty of assessment opportunities to make sure you understand the topics as you go along."

Features well-explained lectures with thoughtful examples

"Excellent explanations. Strong examples. Helpful exercises."

Introduces concepts of open science and the importance of replication

"Excellent course. The rigor that has gone into the video's and downloadable materials is remarkable."

"A great course that goes beyond the mathematics. Mr. Lakens explains statistical inferences in plain English. The assignments help in establishing a critical thinking about p-values and statistical inferences printed in published science."

Teaches learners to critically analyze statistical results and interpretations

"Solid course which taught me how to interpret p-values in a variety of contexts and taught me to not just to consider but (systematic and practical) ways of how to correct for publication bias."

"This course is great! I learned a lot about statistics and how to have a critical thinking about tests and results in the literature. I also gain in confidence for doing statistics."

Includes practical assignments that give learners real-world experience in statistical analysis

"Very good course, with a lot of practical work, which is nice. Also very clear lectures explaining the topics and not too difficult but definitely not too easy exams! Overall fantastic course, which provided me interesting new insights."

"The best course I've ever taken on Coursera (and I've taken a dozen or more). Professor Lakens teaches with great care and attention and all the lessons are meticulously crafted, full of references and extra materials."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Improving your statistical inferences with these activities:

p-value Simulation

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Simulate t-tests to see which p-values are common and which are less common.

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Import the necessary libraries.
Generate a sample of data.
Calculate the t-statistic.
Calculate the p-value.

Effect Size Calculator

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Calculate effect sizes using a statistical software package.

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Import the necessary libraries.
Load your data into the software.
Specify the effect size measure you want to calculate.
Interpret the effect size.

Confidence Interval Calculator

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Use a statistical software package to create confidence intervals.

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Import the necessary libraries.
Load your data into the software.
Specify the confidence level you want.
Interpret the confidence interval.

Three other activities

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Show all six activities

Likelihood Ratio Simulation

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Get an introduction to binomial Bayesian statistics.

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Import the necessary libraries.
Generate a sample of data.
Calculate the likelihood ratio.
Interpret the likelihood ratio.

Power Analysis Calculator

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Use a statistical software package to perform a priori power analyses.

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Import the necessary libraries.
Specify the effect size you expect.
Specify the significance level you want.
Specify the power you want.
Interpret the results of the power analysis.

Replication Study

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Replicate a study from the scientific literature.

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Identify a study to replicate.
Obtain the necessary data and materials.
Conduct the replication study.
Analyze the results.
Write up the results.

Career center

Learners who complete Improving your statistical inferences will develop knowledge and skills that may be useful to these careers:

Biostatistician

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Data Scientist

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Quantitative Analyst

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Statistician

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Market Researcher

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Financial Analyst

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Economist

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Business Analyst

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Actuary

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Data Analyst

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Software Engineer

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Teacher

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Academic Researcher

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Consultant

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Journalist

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