data inference

© Relay Graduate School of Education. All rights reserved. 1

DATA INFERENCE


AGENDA OBJECTIVES

Agenda and Objectives

• Descriptive statistics• Dispersion• Aggregate data• The right questions and graphics• Data inference

Compare basic descriptive statistics and identify their limitations

Describe common mistakes associated with analyzing "on average" data

Explain the purpose of the Data Narrative analyses

Evaluate research questions against criteria for quality

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Data Inference: The Takeaway


“Most car accidents happen within a mile of

your home…



your home…

So you can be sure I’m never coming to your

neighborhood!”



your home…

So you can be sure I’m never coming to your

neighborhood!”

Bad, bad, bad inference!


Inference: Five Points to Consider

1. Basic descriptive statistics don’t always tell the whole story • Two classes with identical mean, median, mode, range are not identical

2. Cutting the data can reveal a richer storyline within the data• SAT scores overall decrease not consistent within disaggregated subgroups

3. Use of statistical concepts requires context and understanding• Standard deviation, statistical significance, “causation”, etc.

4. Small samples can confound trends• Comparing Entertainers vs. Athletes

5. The wrong research question precludes the right inference• Chantix and Century 21

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Closing


2. Did the class do well? Why or why not?

Do Now – We’ve Reviewed This Extensively!

1. What is one fact about the data that you notice?

3. If author Stephen King were to join the class and take this test, how do you predict he might score? Why? There’s no right answer to this last

question! Just for thought…


2. Did the class do well? Why or why not?

Do Now – We’ve Reviewed This Extensively!

1. What is one fact about the data that you notice?

3. If author Stephen King were to join the class and take this test, how do you predict he might score? Why?

Stephen King is a male, so maybe he’d perform like other males in the

30-50 range? He’s an author so maybe more like

Maya Angelou in Class #2?


Exit Ticket – Bonus Questions on the Back!

http://stgdfest.com/?p=1382

http://www.theexitstore.com/TES-EXIT-RW-BB.htm

Click ahead when you’ve completed the appropriate section

of your Handout


You Should Feel Confident With Your Answers. If You Still Have Questions, Review This Session!

http://stgdfest.com/?p=1382

http://www.theexitstore.com/TES-EXIT-RW-BB.htm


Exit Ticket – Question #1. “…ways in which data gets misinterpreted…”

Class #1 and Class #2 had identical mean, median, mode, range, and n-count, although individual performance was quite different. We needed more descriptive statistics.

Additionally, a frequency table with the wrong bin sizes made it appear that Class #1 and Class #2 performed identically.

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Exit Ticket – Question #2. “…average of 1.5 years of academic growth not necessarily…”

An overall average of 1.5 years of academic growth doesn’t say how individual students performed.

With a bimodal distribution, for example, some students perform quite well while others perform quite poorly, and the overall average can still be relatively high.


Exit Ticket – Question #1. “…benefit of disaggregating data…”

Disaggregating data helps uncover trends in performance that go beyond the overall “on average” score.

That said, not every disaggregation will reveal an interesting finding. Disaggregating data is best guided by a mineable, crisp, and meaningful research question.


Thanks for completing the session!