Too much of what passes for “science” today delivers confidence without reliability. Failed predictions, unreproducible studies, statistical rituals, and institutional certainty have become routine features of the landscape. These failures are not mysteries. They follow from models built on weak reasoning, measurements treated carelessly, and a culture that rewards authority over accuracy.
This course begins by confronting that problem directly. Real knowledge is not created by consensus or credentials. It is created by models that make predictions and survive tests. When predictions fail, the model fails, no matter how many experts endorse it. Modern science often avoids this simple requirement, which is why so much published work collapses under replication.
What You Will Learn
We will examine:
- Logic — how arguments work, how conclusions depend on premises, and how errors creep in.
- Induction — how observations support general claims, why this process is rational, and how to detect when someone is abusing it.
- Probability — how to reason when information is incomplete, and how to avoid the false certainty created by p-values and similar shortcuts or ‘rituals.’
These concepts are not reserved for abstractly writing equations on a chalkboard. They are practical tools for evaluating claims in medicine, nutrition, policy, fitness, and everyday decisions, areas where bad reasoning leads to bad outcomes.
Why This Course Exists
If you have ever sensed that something is wrong in the way scientific claims are made, tested, and communicated, you are not imagining it. The replication crisis is evidence enough: vast amounts of research fail when directly evaluated. When predictive power disappears, trust collapses. The solution is not to defer more to authority; it is to understand the structure of knowledge well enough to evaluate claims yourself.
That is the purpose of this course. We will make the underlying ideas clear, demystify math jargon, and show how to use these principles to navigate a world filled with uncertainty and confident claims.
If your goal is to be harder to fool, you’re in exactly the right place.
Curriculum
- 2 Sections
- 12 Lessons
- Lifetime
- Introduction to Epistemology, Logic, and Probability5
- Logic and Advanced Probability Theory7
- 2.1Quantitative Rules in Probability Theory (Qualitative Aspects)
- 2.2Exploring Syllogisms and Logical Fallacies
- 2.3Expanding on Induction and Its Role in Logic and Science
- 2.4Understanding Probability Through the Lens of Logic
- 2.5Logic, Uncertainty, and the Essence of Probability
- 2.6Understanding What Probability Really Is
- 2.7Measuring Nails, Pascals Triangle and the Bell Curve

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