1. SUE BOUDREAU, SCIENCE TEACHER, ORINDA INTERMEDIATE SCHOOL SBOUDREAU@ORINDA.K12.CA.US CATHERINE SALDUTTI, PRESIDENT EDUCHANGE CATHERINE@EDUCHANGE.COM Stories in Statistics: Data Interpretation for Middle School Students

2. Who are you? Cluster by subject area category. Walk to where you are with using data with students, from rarely to often. Share a story about using data with students good or bad.

3. Cool graph 1 Whats the story? Is it true?

4. Cool Graph 1 + additional info. What do you think now? Could you use it?

5. Cool Graph 2 Whats the story? Is it true? Add obituary/graveyard surveys to bring death statistics to life…

6. Cool Graph 3 Teaser… If you want to save $ on your energy bill AND reduce your carbon emissions, what would be the most effective way to save? Clothes line instead of dryer… or what? Discuss with your neighbor.

7. Cool Graph 3 So were you right? What else do you need to know before making changes? Graphic from EnergyStar

8. Great Places to find hot data Your local newspaper NY Times Tuesday Science Section New Scientist Scientific American By the Numbers section Scientific American Mind Consumer Reports Cooks Magazine Discover Magazine Time, Newsweek magazines Mother Jones UN Millennium Development Goals

9. Great places to find hot data Use kids as your search engines – Hot Data in Science assignment….

10. Silly sources of data to seduce students with statistics The Bathroom Readers www.graphjam.com. Students can practice observing, finding trends, making inferences, and asking is it true? www.graphjam.com

11. And more serious reasons students need to understand data Who Cares About Data? student sheet linking data to their lives, health and future.

12. Presentation Focus: Data Interpretation Data Collection – In the lab, school or community – Associated with specific techniques or methods Data Organization – Aligning appropriate organizers to data – Organizer creation & notation Data Interpretation/Analysis –Our Focus today – Identifying Bias – Finding trends & patterns – Understanding the limitations of the interpretation – Problems students have

13. Data Work is Inquiry Work The National Science Education Standards define inquiry as the following: Inquiry is a multifaceted activity that involves making observations; posing questions…using tools to gather, analyze, and interpret data, proposing answers; explanations and predictions…Inquiry requires identification of assumptions, use of critical and logical thinking, consideration of alternative explanations (p. 23).

14. Why Teach Data Skills? The National Research Councils text on Inquiry states: Student understanding of inquiry does not, and cannot, develop in isolation from science subject matter (p. 36). Making data work PART AND PARCEL of content- based science instruction is the way to go teaching one unit on science inquiry and then moving to content is not desirable for authentic science learning.

15. Data in the CA Frameworks Grade 1: Record observations and data with pictures, numbers or written statements… [and] on a bar graph Grade 4: Differentiate observation from inference (interpretation) and know scientists explanations come partly from what they observe and partly from how they interpret their observations. Grade 7: Select and use appropriate tools and technology…to perform tests, collect data and display data. Grades 9-12: Identify possible reasons for inconsistent results, such as sources of error or uncontrolled conditions.

16. Teaching Data Interpretation Identifying compelling data sources Their lab data, from them, about them, their environment, news data, surprising, accessible, topical data. Hooking students methods to get them to care about these data Guess and check, find the story, Hot issues in Data. Querying Data How its gathered, graphed and where it comes from all matter. Problems students have Steps to stories with statistics Prediction, understanding, observing, trends, inferences and surprises. From statistical stories to science Identify bias, tie data to conservative conclusions, triangulate information, evaluate citations.

17. Identifying Compelling Data Sources Their own data from experiments and surveys. Data they are part of. Family data, attendance, Ca Healthy Kids… Data relating to current unit in the news. Data they want to know for project work or for their own decisions. Health data in the news they relate to – obesity, asthma, diabetes, teen pregnancy, US eating & exercise habits etc. Environment data in the news – especially local/state and good news OR data that shows a way to do something positive. Data that is fairly easy to understand Surprising data – good news where they thought it was terrible, insights ex. happiness vs income.

18. Hooking students with their own survey: Blind taste-testing organic vs ordinary foods

19. Hooking students with data they want to know: Take Action Project

20. Hooking students with family stories – Will the diseases grandparents knew be different to now? Try the disease scavenger hunt with the people next to you. See The Hunt is On from SEPUP. Look over the disease survey. Analyze family survey for cause of disease. See the Patterns of Disease analysis sheet. Find trends by cause. Make inferences to explain. These questions guide the unit.

21. Hooking students with family stories Data collection

22. Hooking students with family stories: Analysis

23. Hooking students with s urprising data in Science and Life Issues: Medicine Testing Students discover the placebo effect

24. Hooking students with data they are a part of: Has there been an epidemic at school this fall? What trends in attendance do they expect? See the activity sheet and data. Students graph their attendance data for fall this year and last.

25. Hooking students: Setting up a unit on microbes – Has there been an epidemic at school this fall? What surprises them? Was there an epidemic? What questions do they have for the upcoming unit? What questions about the data – gaps? Causes of absence etc.

26. Querying Data – How data is gathered matters Dr. Stupid finds that women have 52% faster reaction times than men. Is it true? Watch and note the errors.

27. Querying Data – What was wrong with Dr. Stupids data collection and analysis? Mistakes include bias, poor measurement, not repeated, over-interpretation of data (gaps and silences).

28. Querying Data - How the data is graphed matters Mortality statistics data graphed in 4 ways. Work in groups of 4. Each does one graph plus the questions. Share what students might learn from each of your graphs within your group. But be careful not to leave the wrong way as the last thing they learn. End with an example of good practice.

29. Querying Data: How its graphed matters.

30. Querying Data - Where it comes from matters Precise statistics sound accurate, but are they? - The Oh yeah? attitude pays in science - Always have students consider sources of error and the resulting significance of their data. Which is accurate? You only use 10% of your brain Drink 8 glasses of water a day Neither. Both are examples of incestuous reporting. See www.snopes.com www.snopes.com

31. Querying Data - Subtle Bias and Information Literacy Teach students how to be website detectiveS : Masthead, about us, sponsors and mission statement. Follow the money – is it commercial, and how will that bias the information? Ex. Airborne, WebMd Does it have political or religious bias? Ex. http://www.viewzone.com/endtime.html, www.peta.com http://www.viewzone.com/endtime.htmlwww.peta.com Google the organization and sponsors. How was the data collected? TRIANGULATE data – Extraordinary claims require extraordinary evidence – B.Nye

32. Problems Students Have Plotting data on a consistent scale. Observing the data – understanding axes. Separating observation from inference. Direct instruction, bad graph activity, practice and leveraging with math. Finding trends and patterns, especially the unexpected. Direct instruction with examples ex. epidemic curves. Class culture that its fine to modify your hypothesis, mistakes are for learning. Finding, evaluating and CITING SOURCES. Fairness, respect, makes your work look good. Grade it hard. The bad data can be so entertaining that kids remember that instead of the right way to do it. Use bad practice to highlight good practice the next day. Spend more time on good practice.

33. Steps to a story with statistics Prediction is the story hook Relevance to their lives is the setting Understanding the axes, scale, observing the data points is the premise. The trends & patterns is the narrative and the surprise twist in the plot Inferences are the possible conclusions

34. From Statistical Story to Science Scientists are skeptical of extraordinary claims and surprises. They identify bias. They tie data to conservative conclusions. Triangulate data and information, including some primary sources. Find how the data was collected.

35. Surprising patterns in reliable data motivate further research, leading to testable hypotheses and new discoveries – a very happy end to a story in statistics! From Statistical Story to Science The End

36. Reliable and Cool Data Sources National Center for Health Statistics at Center for Disease Control and Prevention: http://www.cdc.gov/nchs/ FluView: http://www.cdc.gov/flu/weekly/ http://www.cdc.gov/nchs/http://www.cdc.gov/flu/weekly/ Nationmaster Statistics http://www.nationmaster.com/statisticshttp://www.nationmaster.com/statistics World Health Organization Statistics Information Source: http://www.who.int/whosis/en/ http://www.who.int/whosis/en/ Guttmacher Institute advancing sexual and reproductive health world wide – table maker. http://www.guttmacher.org/tablemaker/index.mhtml http://www.guttmacher.org/tablemaker/index.mhtml www.snopes.com to check out possible internet urban legends. www.snopes.com Your local newspaper NY Times Tuesday Science Section New Scientist Scientific American By the Numbers section Scientific American Mind Consumer Reports Cooks Magazine Discover Magazine Time, Newsweek magazines Mother Jones UN Millennium Development Goals http://www.un.org/millenniumgoals/http://www.un.org/millenniumgoals/

37. Contact Information Sue Boudreau, Orinda Intermediate School sueboudreau2004@yahoo.com sueboudreau2004@yahoo.com Catherine Saldutti, EduChange catherine@educhange.com catherine@educhange.com Link/site to download content: www.orindaschools.org, go to OIS, go to Teachers, go to Boudreau. See Stories in Statistics www.educhange.com/the_news.htm. www.orindaschools.org www.educhange.com/the_news.htm

38. How are all these factors related? Using data to start the Take Action Projects From New Scientist Feb.08