2. Key ideas of analysis & interpretation of data Visualize data – (tables, pictures, graphs, statistics, etc. to reveal patterns & relationships). Making sense! Data becomes evidence to support an argument or explanation to a scientific question (or can lead to more questions). Provides insight into how well an investigation was designed and carried out.

3. Why Data Analysis & Interpretation is Hard The word “Data” is plural

4. He is 180 cm tall (that is a “fact”!) How tall is this boy?

5. 180 cm 152 cm 146 cm 140 cm How tall is this group of children? Now we have “data” (and a more complicated answer)

6. Which boy is taller? … a pretty easy question to answer

7. Which group is taller? … a more complicated question to answer

8. Talking about a group usually means dealing with variability

9. You are a Park Ranger stationed at Old Faithful in Yellowstone National Park, how would you answer Park visitors when they arrive at Old Faithful and ask you "How long until the next eruption? " What could you tell visitors hoping to watch the geyser erupt to help them decide whether to wait or to go visit the restroom or get lunch first? To see if the time between eruptions follows a predictable pattern, graduate students observed Old Faithful around the clock for two weeks in August, 1990, and measured the time in minutes between every eruption. The table presents the minutes they recorded for each 24-hour period during the two weeks. How would you use the data to give Park visitors a useful answer to their question, “How long until the next eruption?” Play with Data

10. Visualize Data "How long until the next eruption?"

11. How do these words describe and what do they say about the data? K-2 Compare, more, less, how many, total, 3-5 Difference, middle, spread out, clumped 6-8 Mean, median, mode, center, range, spread, shape, variability, pattern, sample, random, outlier, frequency, probability, bivariate, scattered, clustering, positive or negative correlation, linear relationship,nonlinear relationship, slope, closeness to line, categorical, numerical, shifted,biased, even, slightly Describing Words 9-12 Distribution related to center (mean, median), spread (interquartile range, standard deviation), normal, skewed, correlation coefficient, correlation vs. causation, bi-modal

12. There are THREE kinds of questions that students are most likely to encounter in high school and college science courses! Connecting the three practices: Asking questions, planning and carrying out investigations, and analyzing & interpreting data Scientists make observations and measurements (collect data) with a question in mind. Compare two or more groups in a single variable Correlation: See how strongly two variables are correlated with each other Time series: See how a measurement changes through time Variability is often forgotten! The question asked often suggests the kind of graph to make

13. 1.Which car manufacturer makes the most fuel-efficient fleet of vehicles, Chevrolet or Toyota? 2.Has Penobscot Bay warmed over the last 10 years? 3.Which beam shape supports the most weight? 4.How deep are the lakes in Maine? 5.How tall is this tree? 6.Is there a relationship between wind speed and air pressure? a.Compares groups b.Correlation c.Time series d.Variability e.Not a statistical question What kind of question is each of the following?

14. The best kind of graph to use depends on what kind of question you are asking. 1. Variability within a group?  frequency plot 2. Compare groups?  2 frequency plots or 1 bar graph 3.Correlation between two variables?  scatter plot 4. Change through time?  line graph

15. Graph Choice Chart: Link choice of graph type to a question

16. Maine Data Literacy Project http://participatoryscience.org The Data Literacy Framework is built upon two foundational skills: (1)Visualize variability in a data set using frequency plotsVisualize variability in a data set using frequency plots (2) Describe variability in dataDescribe variability in data Once students learn to expect data to vary, and can show and describe variability in a simple data set, they then learn to (3) Display and interpret data in terms of a stated question. Display and interpret data in terms of a stated question. As students continue to graph and describe data and variability, they develop ability to (4) Explain their reasoning for how the graph (evidence) supports their claimExplain their reasoning for how the graph (evidence) supports their

17. Frequent exposure to graphs. Students create, describe, interpret or discuss data in graphs at least weekly. Repeat Framework steps 1-4 with increasingly complex data sets, and with increasing levels of skill and detail. Students might begin comparing groups using small, univariate data sets that can be graphed by hand, and move to describing correlations and time series using larger or more complex data sets with two variables, and then use data sets that may require reorganization, summarizing, or sub-sampling using spreadsheet and graphing software. Relate data and graphs to curriculum content. For example, comparison of variability in water and air temperature data at a NOAA buoy location is real-world evidence for the phenomenon of specific heat. Base interpretation and reasoning about data on visual inspection before learning to apply statistical tests to determine significance. Draw graphs by hand before using the computer. Evidence supports that students learn data analysis through:

18. Time in grade span groups!