Distance Learners:
Below is the lesson plan that you usually see me write on the dry erase board in class.
I have set up the exact same class on a Google Doc in your CSP class folder. This will allow you to enter "Journal" questions I pose to you and other Activities. Thus, I recommend you go straight to the Google Doc:

Access Google Doc I set up for you in your CSP folder titled:   “U4Ch1L3_CheckingYourAssumptions”
When you complete today's class assignments, Email me at:  "john.harrington@student.sthelenaunified.org"  so that I can give you credit.

U4Ch1L3_Checking Your Assumptions

Purpose: Decisions based on data, rest on assumptions about the data as well as the data itself. Students will learn that incorrect assumptions about a dataset can lead to faulty conclusions.  Identifying and either validating or disproving assumptions is a very important part of Data Analysis.
Note: Last class, you looked at a Data Trend and made an Assumption…was it correct?

Video:  Google Flu Trends

Journal: What are the potential beneficial effects of using a tool like Google Flu Trends?

• Select one of the articles below. Explain the following:  “Why did Google Flu Trends eventually fail?
• Wired - What Can We Learn from the Epic Failure of Google Flu Trends?  
• NYTimes - Google Flu Trends: The Limits of Big Data  
• Nature - When Google got flu wrong  
• Time - Google’s Flu Project Shows the Failings of Big Data  
• Harvard Business Review - Google Flu Trends’ Failure Shows Good Data > Big Data

Activity:  The Digital Divide and Checking your Assumptions

• View the Pew Research “Digital Divide” Graphs: Internet/Broadband Fact Sheet.
• Scroll down the page and view not only the graphs, but click on the comparison groups for each (Age, Race, Gender, Income, Education & Communtiy.)
• Define Digital Divide.
• What groups are under-represented or over-represented?
• Select a scenario/graph that you find interesting and explain why.
• What kind of trends or patterns might the decision makers in your scenario be observing in the data when they make their decision? How might they use these patterns to help make their decision?
• The people making decisions in each instance have not taken into account the “digital divide.” How has the assumption that all people have equal access to technology possibly biased their analysis?