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© Relay Graduate School of Education. All rights reserved. 1
DATA INFERENCE
© Relay Graduate School of Education. All rights reserved. 22
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
© Relay Graduate School of Education. All rights reserved. 33
Data Inference:
The Takeaway
© Relay Graduate School of Education. All rights reserved. 44
“Most car accidents
happen within a mile of
your home…
© Relay Graduate School of Education. All rights reserved. 55
“Most car accidents
happen within a mile of
your home…
So you can be sure I’m
never coming to your
neighborhood!”
© Relay Graduate School of Education. All rights reserved. 66
“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!
© Relay Graduate School of Education. All rights reserved. 7
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
© Relay Graduate School of Education. All rights reserved. 88
Closing
© Relay Graduate School of Education. All rights reserved. 9
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…
© Relay Graduate School of Education. All rights reserved. 10
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?
© Relay Graduate School of Education. All rights reserved. 11
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
© Relay Graduate School of Education. All rights reserved. 13
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
© Relay Graduate School of Education. All rights reserved. 14
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
© Relay Graduate School of Education. All rights reserved. 15
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.
© Relay Graduate School of Education. All rights reserved. 16
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.
© Relay Graduate School of Education. All rights reserved. 1717
Thanks for completing
the session!

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Data Inference

  • 1. © Relay Graduate School of Education. All rights reserved. 1 DATA INFERENCE
  • 2. © Relay Graduate School of Education. All rights reserved. 22 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
  • 3. © Relay Graduate School of Education. All rights reserved. 33 Data Inference: The Takeaway
  • 4. © Relay Graduate School of Education. All rights reserved. 44 “Most car accidents happen within a mile of your home…
  • 5. © Relay Graduate School of Education. All rights reserved. 55 “Most car accidents happen within a mile of your home… So you can be sure I’m never coming to your neighborhood!”
  • 6. © Relay Graduate School of Education. All rights reserved. 66 “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!
  • 7. © Relay Graduate School of Education. All rights reserved. 7 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
  • 8. © Relay Graduate School of Education. All rights reserved. 88 Closing
  • 9. © Relay Graduate School of Education. All rights reserved. 9 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…
  • 10. © Relay Graduate School of Education. All rights reserved. 10 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?
  • 11. © Relay Graduate School of Education. All rights reserved. 11 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
  • 13. © Relay Graduate School of Education. All rights reserved. 13 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
  • 14. © Relay Graduate School of Education. All rights reserved. 14 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
  • 15. © Relay Graduate School of Education. All rights reserved. 15 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.
  • 16. © Relay Graduate School of Education. All rights reserved. 16 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.
  • 17. © Relay Graduate School of Education. All rights reserved. 1717 Thanks for completing the session!

Editor's Notes

  1. Say: Greetings friends. Happy to have you with us.   We will circle back to the warm up throughout the next 90 minutes, as we work tirelessly toward being able to answer those three questions.
  2. Give: G/S’s 30 seconds to read today’s objectives, also on your interactive handout, pg. 1   Say: Here’s our agenda for the day, also in your interactive handout pg. 1. A couple thoughts on our pacing for the day…
  3. Optional Turn & Talk: Tell your partner 1-2 concepts that stuck with you today
  4. Review 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
  5. Closing - return to Warmup to answer again   If Stephen King were to join the class and take this test, how do you predict he might score? Why?   ASR: He’s a male so he might perform like the men who didn’t perform as well (ranging 30’s-50’s) He’s an author he might perform like Maya Angelou in class two, she got a 100 If he just joined the class, he’ll get a 0   Exactly, we aren’t sure, but we have some data points that can help up and questions to consider. Now you’re connoisseurs! He’s just an alcoholic in Maine living in a shed writing horror novels.
  6. Closing - return to Warmup to answer again   If Stephen King were to join the class and take this test, how do you predict he might score? Why?   ASR: He’s a male so he might perform like the men who didn’t perform as well (ranging 30’s-50’s) He’s an author he might perform like Maya Angelou in class two, she got a 100 If he just joined the class, he’ll get a 0   Exactly, we aren’t sure, but we have some data points that can help up and questions to consider. Now you’re connoisseurs! He’s just an alcoholic in Maine living in a shed writing horror novels.
  7. Exit slip BONUS questions on the back! Have some fun! http://www.theexitstore.com/TES-EXIT-RW-BB.htm http://stgdfest.com/?p=1382
  8. Exit slip BONUS questions on the back! Have some fun! http://www.theexitstore.com/TES-EXIT-RW-BB.htm http://stgdfest.com/?p=1382
  9. Exit slip BONUS questions on the back! Have some fun! http://www.theexitstore.com/TES-EXIT-RW-BB.htm http://stgdfest.com/?p=1382
  10. Exit slip BONUS questions on the back! Have some fun! http://www.theexitstore.com/TES-EXIT-RW-BB.htm http://stgdfest.com/?p=1382
  11. Exit slip BONUS questions on the back! Have some fun! http://www.theexitstore.com/TES-EXIT-RW-BB.htm http://stgdfest.com/?p=1382
  12. Optional Turn & Talk: Tell your partner 1-2 concepts that stuck with you today