Description
Statistical Inference: How to Draw Conclusions from Data
Can you trust your data? This **Johns Hopkins course** teaches the core principles of statistical inference: probability, variability, confidence intervals, hypothesis testing, and resampling methods—so you can distinguish real signals from noise.
Key Concepts
- Probability—conditional probability, Bayes’ rule
- Expectation & variance—the language of uncertainty
- Confidence intervals—quantify estimation uncertainty
- Hypothesis testing—p-values, type I/II errors, power
- Resampling—bootstrapping and permutation tests
Why This Matters
Without inference, data science is just pattern-spotting. This course gives you the **rigor to make credible claims**.
Who Is This For?
- Researchers designing experiments
- Analysts evaluating A/B tests
- Data scientists needing to validate model performance
- Students in the JHU Data Science Specialization
Think Like a Statistician
You’ll learn to ask: “Could this have happened by chance?”—and answer it confidently.
Ready to move beyond description to inference? Enroll now.
