Short summary Hands-on statistics for measurement data: distributions, descriptive stats, hypothesis testing, outliers, and the central limit theorem.
Learning objectives
Compute and interpret mean, variance, confidence intervals.
Apply t-tests, chi-square tests, and identify outliers.
Relate histograms to probability distributions and sampling variability (CLT).
Key concepts (brief)
Sampling distributions and the Central Limit Theorem.
When to use t-distribution vs normal approximation.
Robust statistics and practical outlier handling.
Recommended notebooks to run
basic_statistics.ipynb
distributions.ipynb
t-test.ipynb
Central_limit_theorem_illustration.ipynb
outliers_example.ipynb and outliers_example_pairs.ipynb
Suggested exercises
Use bootstrap or t-test to compare two small-sample datasets.
Detect and justify removal/retention of outliers in a measurement series.
Prerequisites Introductory probability and basic Python (NumPy, matplotlib).