Improving your statistical inferences

This course is designed to assist you in making more accurate statistical inferences from empirical research. First, this course will cover how to read p-values, effect sizes, confidence intervals, Bayes Factors, and likelihood ratios appropriately, as well as how these statistics can be used to answer a variety of queries. Then you'll learn how to construct experiments with a low false positive rate and how to choose the right sample size for your study, such as to obtain high statistical power. Following that, you'll learn how to analyze evidence in the scientific literature in light of pervasive publication bias, such as through p-curve analysis. Finally, this course will cover how to conduct philosophy of science, theory creation, and cumulative science, as well as how to conduct replication studies, why and how to pre-register your experiment, and how to disseminate your findings in accordance with Open Science principles.

You will learn how to simulate t-tests to determine which p-values to expect, calculate likelihood ratios and gain an introduction to binomial Bayesian statistics, and learn about the positive predictive value, which expresses the probability that published research findings are true, through practical, hands-on assignments. This course will walk you through the issues with optional stopping and teach you how to avoid them by using sequential analyses. You'll calculate effect sizes, do a-priori power analyses, and explore how confidence intervals function through simulations. Finally, you'll learn how to use equivalence testing and Bayesian statistics to determine whether the null hypothesis is true, as well as how to pre-register a study and share your data on the Open Science Framework.

Skills you will gain

• Likelihood Function
• Bayesian Statistics
• P-Value
• Statistical Inference

Instructor: Daniel Lakens

Coursera rate: 4.9/5.0, 716 ratings

Offered by: Eindhoven University of Technology

Enroll here: https://www.coursera.org/learn/statistical-inferences