Describing Data: Statistics & Uncertainty

This lecture covers the basics of descriptive statistics, quantifying uncertainty, and relating two variables. Topics include robust statistics, power-law distributions, generalized means, hypothesis testing, confidence intervals, and the importance of understanding p-values. The instructor emphasizes the significance of choosing the right statistical tests, interpreting correlation coefficients, and avoiding common misconceptions. Practical examples and tools are provided to illustrate concepts, such as the use of error bars, bootstrap resampling, and Simpson's paradox. Students are encouraged to critically analyze data, consider the implications of statistical findings, and be cautious when drawing conclusions.