Some work-in-progress illustrations to gently introduce some concepts for 2-sample t-tests & how to think about p-values. Meant as teaching aids, not as a comprehensive standalone lesson. Caveats & assumptions abound. 🧵 
Thinking warm-up:
ie
Meet p-values
p-values continued
Comparing scenarios & connecting sample differences to p-values to evidence for population differences.
significance level
But couldn't our conclusion be wrong? Heck yes. Type I errors (based on your samples you conclude there is a significant difference in means, when there actually isn't)
Or Type II errors (based on your samples you conclude there isn't a significant difference, when there actually is)
& remember: the p-value is not enough. Discuss/report differences in other (often more meaningful) ways. Data visualization, actual differences in context, effect sizes, etc.
