The document outlines an agenda and objectives for a session on data inference. The session will cover descriptive statistics, dispersion, aggregate data, asking the right questions and using the right graphics, and data inference. It will compare basic descriptive statistics and identify limitations, describe mistakes in analyzing average data, explain the purpose of data narratives, and evaluate research questions. The document provides examples of bad inferences from average data and outlines five points to consider in data inference, including that descriptive statistics don't tell the whole story, cutting the data can reveal more, statistical concepts require context, small samples can confound trends, and research questions shape inferences.

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DATA INFERENCE

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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
2 2

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Data Inference:
The Takeaway

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“Most car accidents
happen within a mile of
your home…

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“Most car accidents
happen within a mile of
your home…
So you can be sure I’m
never coming to your
neighborhood!”

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“Most car accidents
happen within a mile of
your home…
So you can be sure I’m
never coming to your
neighborhood!”
Bad, bad, bad
inference!

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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
7

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Closing

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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…

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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?

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Exit Ticket – Bonus Questions on the Back!
http://stgdfest.com/?p=1382
http://www.theexitstore.com/TES-EXIT-RW-BB.htm

12. Click ahead when
you’ve completed the
appropriate section
of your Handout

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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

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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.
5
5
5
5

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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.

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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.

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Thanks for completing
the session!