
Introductory Case Study
Course Navigation
Course and Instructor Introduction, and Course Roadmap
Introduction, and a real-world example !
Survivorship Bias, and how it makes your Data incomplete
Survivorship Bias, real world applications and precautions
The Dangers of Selection Bias
The Law of Small Numbers
Deep Data Distortions, Part 1
Deep Data Distortions, Part 2
Deep Data Distortions, Part 3
AI and Distorted Data
Section Recap and Conclusion
Introduction, and Examples
Central Tendencies, Range, Variance, and Standard Deviation
Deciding based on Averages - The Dangers !
Misleading Visual Displays, Part 1
Misleading Visual Displays, Part 2
Simpson's Paradox, Real World Example 1
Simpson's Paradox, Real World Example 2
Cherry Picking, Publication Bias, and the Replication Crisis.
Cherry Picking, Statistical Significance, and the p-value.
p-Hacking, and Section Recap.
Introduction, and the Gambler's Fallacy
The Hot Hand Belief
The Clustering Illusion
The Illusion of Control
Regression to the Mean
Fooled by Randomness
Section Recap
Practice Exercise 1
Section Introduction, Heuristics and Biases, dangerous shortcuts !
Bias 1 : The Availability Heuristic and its dangers !
Bias 2 and 3 : The Representativeness Heuristic and Base Rate Neglect, Part 1
Bias 2 and 3 : The Representativeness Heuristic and Base Rate Neglect, Part 2
Practical implications of Representativeness and Base Rate Neglect.
Bias 4 : The Conjunction Fallacy and its dangers !
Bias 5 : Overconfidence, and it's dangers !
Practical Implications of the Overconfidence Bias.
Section Recap
Section Introduction, and Expected Value
Loss Aversion, and its dangers !
The Normal Distribution, and its Assumptions
The Limitations of the Normal Distribution : Mediocristan and Extremistan !
Black Swan Events
Model Risk
Section Recap
Strategy 1. Question the Data Before You Trust It
Strategy 2. Choose the Right way to Present the Data, Not the Easy, or Good Looking One
Strategy 3. Treat Randomness with Respect
Strategy 4: Know When to Override Your Intuition
Strategy 5 : Evaluate Risk by Outcomes and Exposure, Not Just Normal Distributions
Strategy 6: Run a Pre-Mortem Before You Begin
Strategy 7: Build a Culture That Rewards Good Process, Not Just Good Outcomes
A Practice Exercise, with Lessons, for Application of the Learnings from this Section of the Course !
Course Conclusion
Research References and Sources for the entire Course
How to download your UDEMY CERTIFICATE, PDF enclosed.
Every day, as a Leader or Manager, you make decisions based on data. You read a report. You look at a chart. You trust a number. And you act !
But what if the data was already broken before you looked at it? What if the chart was technically accurate but completely misleading? What if the pattern you spotted was pure randomness, and your brain simply invented a story around it?
This is not a rare problem. It happens to all of us, in every organization, every industry, every day.
Research tells us that the human brain is not naturally wired to handle statistics, probability, randomness, and risk accurately. We make the same systematic, predictable mistakes — not because we are careless, but because of how our brains work !
In this Course, you will learn exactly how and why this happens. And what to do about it.
We go through flawed data, misleading summaries, pattern errors in randomness, probability misjudgments, risk blind spots, and practical strategies you can apply right away. Every concept is backed by real research and real-world examples.
Sivakami has nearly 2 decades of corporate experience leading global teams at organisations like Microsoft and Verizon; and nearly a decade teaching Leadership Science and Psychology to more than 160,000 students across 180 countries. So you are in good hands !
This is not a Probability or Statistics course. You will not be taught foundational Probability or Statistics concepts here.
A basic high school level understanding is all you need.
What you will learn is how to stop misreading the data you already have — and start making decisions that are sharper, more honest, and far more reliable. How to make good decisions, when it involves probability, statistics, randomness, risk and uncertainty !
If your decisions affect teams, budgets, strategies, or people — this Course is absolutely for you !
Declaration : AI Tools only used for minor language edits, and in searching for Research Papers and Sources. NotebookLM used for Infographics and Slides. No other AI use !