Mindware: Critical Thinking for the Information Age

Learn how to think critically and analytically

This four-week critical thinking course presents basic concepts from statistics, probability, scientific methodology, cognitive psychology and cost-benefit theory and shows how they can be applied to everything from picking one product over another to critiquing media accounts of scientific research.

Apply concepts of probability theory, the scientific method and microeconomics to judgments

Most careers and professions these days require more than general intelligence. In addition, they require the ability to collect, analyse and think about data.

This course will teach you how the basic concepts of statistics and probability – including the concepts of variables, normal distribution, standard deviation, correlation, reliability, validity, and effect size – and how these can be applied into your daily life.

You’ll also learn how to conduct a cost benefit analysis, and will learn how to accurately assess whether two variables are related to one another, and how to avoid false or illusory correlations.

Evaluate and critique reports of scientific findings within the media as well as cognitive biases

You’ll then decipher why experiments provide far better evidence about causality than correlations, and will evaluate and critique reports of scientific findings within the media.

You’ll also reflect on the most pervasive and important cognitive biases (or inference procedures) that are both rapid and automatic, but which usually produce incorrect results. Ultimately, the end result of this course will help you to develop a broad understanding of how to make good decisions.

Learn from critical thinking experts at the University of Michigan

You’ll be learning from leaders within the critical thinking field at the University of Michigan and will be given expert advice throughout.

What topics will you cover?

• Basic concepts of statistics and probability including the concepts of variable, normal distribution, standard deviation, correlation, reliability, validity, and effect size
• How to conduct a cost-benefit analysis, and why you should throw the analysis away after doing it if the decision is personal and very important
• How to accurately assess whether two variables are related to one another, and how to avoid false or illusory correlations
• Why experiments provide far better evidence about causality than correlations
• Compare logical and dialectical reasoning and gain an understanding of what conclusions may be drawn when one form of thinking is used over the other