Ementorhub

What you'll learn

Learn the rationale and limitations of significance testing within scientific inquiry, including crafting hypotheses and interpreting p-values

Learn how scientific experiments are proposed, designed, reviewed, and published

Identify common biases and mistranslations in science research reporting and the challenges of conveying science research to the public effectively

Assess the credibility of claims about research and recognize the role of replicability and generalizability in scientific progress

Skills you'll gain

Research

Media and Communications

Data Literacy

Probability & Statistics

Scientific Methods

Research Design

Peer Review

Statistical Methods

Experimentation

Statistical Inference

Statistical Hypothesis Testing

Data Analysis

There are 4 modules in this course

During the course, you’ll explore the nuances of significance testing, scientific research methods, and science communication, emphasizing the importance of carefully interpreting statistical results. After learning about the scientific process, you’ll learn how science can make its way into the news cycle—and how critical context can be lost amidst the noise. By the end of the course, you’ll be able to think more critically about the media you consume and how you can view science news and information with a more nuanced perspective. This is the second course in Understanding Data: Navigating Statistics, Science, and AI Specialization, in which you’ll gain a core foundation for statistical and data literacy and gain an understanding of the data we encounter in our everyday lives.

Experimental Design

How Science Becomes News

Science and Society