Friday 24 November 2023

Improving your statistical inferences (Free Course)


There are 8 modules in this course

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.

Join Free - Improving your statistical inferences


Post a Comment

Popular Posts


AI (23) Android (24) AngularJS (1) Assembly Language (2) aws (16) Azure (7) BI (10) book (3) Books (92) C (77) C# (12) C++ (82) Course (60) Coursera (167) coursewra (1) Cybersecurity (22) data management (9) Data Science (68) Data Strucures (6) Deep Learning (9) Django (6) Downloads (3) edx (2) Engineering (14) Excel (13) Factorial (1) Finance (5) flutter (1) FPL (17) Google (17) Hadoop (3) HTML&CSS (46) IBM (19) IoT (1) IS (25) Java (92) Leet Code (4) Machine Learning (37) Meta (18) MICHIGAN (4) microsoft (3) Pandas (3) PHP (20) Projects (29) Python (693) Python Coding Challenge (135) Questions (2) R (70) React (6) Scripting (1) security (3) Selenium Webdriver (2) Software (17) SQL (38) UX Research (1) web application (8)


Person climbing a staircase. Learn Data Science from Scratch: online program with 21 courses