Statistics — Introduction and Learning Goals

Statistics — Introduction and Learning Goals#

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).

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