Starts on the Date/Time - Jan 25 2017 - 12:45 pm - 1:45 pm
These days, people claim their decisions are “data driven.” But what does that really mean? After all, data can be used used to say, well, whatever a person wants them to say! Is it true that 96.5% of all statistics are made up? Or, did I just make that up? Given that the average person probably doesn’t know what it means when we talk about averages, and we have a real gap in understanding.
Learning to “talk back to statistics” means you’ll be prepared in a data-driven society to actually understand what the data are saying, and what they aren’t. We’ll look at how data becomes biased in its collection, analysis, representation, and interpretation. We’ll try to figure out what it means to actually be fair. We will do our best to create methods that give the most unbiased results possible, and learn how to manipulate data so that we can catch others when they are doing it!
This class will be heavy in discussion, with accommodations made for students who prefer to take more time to think before responding. We will look at many examples of data in various stages, mostly real, some crafted to demonstrate the issues that can arise. Students will try their hands at the art of data manipulation. Students are not expected to have prior knowledge of statistics, as we will learn the content required to understand manipulation alongside the actual manipulation. Students will learn how to collect, analyze, represent, and interpret data, as well as how each of these stages can be done in a way that introduces bias.
Instructor: Lisa Fontaine-Rainen
5-10 students, 10 years and older
Time: Wednesday 12:45-1:45 pm Pacific.
Weekly. Jan 24 – May 9, 2017. Spring Break TBD
Weekly. Jan 23 - May 9, 2017. Spring Break TBD by instructor.
Class Registration Fee: $245.00 (GHF Members receive a $15 discount off of each class they sign up for)
GHF Members must be logged in to access the Member-only GHF Online registration form.
Week 1: Introduction to statistics, pre-assessment, discussion of “What is fair?”
Week 2: Questionnaires, leading questions, question order, background information
Week 3: Sampling methods, why sampling is important, when sampling goes wrong
Week 4: Science and data gathering, including the importance of the control, changing a single variable, and basic experimental design
Week 5: The concept of “average,” including mean median, mode, when to use each, and how to be sure which one you’re hearing
Week 6: Data analysis beyond the average, including other methods of crunching the numbers, what they mean, what they don’t, and margin of error.
Week 7: Graphs week 1: Ways to display those numbers that trick the eyes!
Week 8: Graphs week 2: More ways to make those numbers look all out of whack!
Week 9: The semi-attached figure: Getting people to think what you want by showing them something else.
Week 10: Post hoc ergo propter hoc: Correlation vs. causation
Week 11: Logical fallacies continued: A look at other logical fallacies and how they can impact thinking about data and statistics.
Week 12: Statisculation: A review of some of the other nasty things people can do, sometimes without even realizing it!
Week 13: Summary of talking back to a statistic, development of steps to ensure you have examined a statistic well. A chance to really tackle some good examples!
Week 14: A week built in to go off on tangents that arise, make-up anything we fall behind on, or explore something the students wish to explore
Week 15: Wrap up discussion, post-assessment