1. Lecture PowerPoint Slides Basic Practice of Statistics 7 th Edition

2. In chapter 0 we cover … Where the data come from matters Always look at the data Variation is everywhere What lies ahead

3. Getting started Does Soundscan know what’s hot in popular music? Is hormone therapy appropriate after menopause? What are the extent, cause, and possible solutions to global climate change? Statistics is the science of learning from data.

4. Data are numbers, but … “Congratulations, your baby weighs 8.5!” 8.5 what? Kilograms? That’s a BIG baby! Data are numbers with a context. Likewise, we will use our human judgment to guide our use of statistical tools, such as graphs and calculations.

5. Where the data come from matters Observational studies versus experiments Early hormone replacement studies: observational. Challenge: lurking variables, such as education and affluence of patients Later, definitive studies: experiments, decided no reduction in heart attack risk due to therapy Just one example of complex relationship between variables being oversimplified

6. Always look at the data Example graph: A few carefully chosen graphs are often more instructive than great piles of numbers.

7. Just ALWAYS do experiments? Can’t. Isn’t always necessary: well-designed observational studies, such as sample surveys, can yield high-quality information Well-designed ≠ large number of respondents, necessarily Example: Ann Landers’ reader survey

8. Variation is everywhere Gas prices, June 1990 to August 2013:

9. Because variation is everywhere, Conclusions are uncertain. Example: efficiency of HPV vaccine “was 98% (95 percent confidence interval 86% to 100%).” Truth? It’s in there … or not … Variability is inescapable.

10. What lies ahead Purpose: give a working knowledge of the ideas and tools of practical statistics. Three main areas: Data analysis (Chapters 1–6) Data production (Chapters 8 and 9) Statistical inference (Chapters 13–18, 20–27)

11. Doing statistics is … More than manipulating numbers! You must: State a problem in its real-world context, Plan your specific statistical work in detail, Solve the problem by making the necessary graphs and calculations, and Conclude by explaining what your findings say about the real-world setting.