Thinking warm-up:pic.twitter.com/94d8kqJSVW
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Comparing scenarios & connecting sample differences to p-values to evidence for population differences.pic.twitter.com/9qrHVoDS76
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)pic.twitter.com/HnlXKZEi38
Or Type II errors (based on your samples you conclude there isn't a significant difference, when there actually is)pic.twitter.com/PuMFyYSO1A
& 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.pic.twitter.com/E7nuASmYIA
This is excellent work. Can you provide information on the possibility of sharing this with students, and if so, how attribution can take place correctly? Any chance this is part of a book teachers/students can purchase?
Sure thing, they're all here open & free for use (these ones are in the 'other-stats-artwork' folder):https://github.com/allisonhorst/stats-illustrations …
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