Learn why false discoveries plague data science, how to detect statistical ghosts, and proven frameworks to prevent false positives. Includes real-world case studies, multiple testing corrections, and workflow solutions.
Learn the difference between Type 1 (false positive) and Type 2 (false negative) errors with real-world examples from healthcare, business and tech. Discover practical strategies to avoid costly mistakes in decision-making.
Master p-values and significance levels with real-world examples. Learn how to avoid common misinterpretations, choose proper alpha levels, and apply statistical significance correctly in research and A/B testing.
Learn when and why the dependent variable changes in experiments, with practical tips on detecting real effects, avoiding pitfalls like confounding variables, and improving statistical analysis. Essential guide for researchers and analysts.