The document discusses how to handle uncertainty in public data, focusing on identifying doubts in numbers, analysis, and understanding the margin of error. It highlights the importance of distinguishing between counts and estimates and emphasizes the significance of sample size on data reliability. Key takeaways include knowledge of data sources, types of data, and appropriate interpretation of margins of error.

1. Briana Preslar
Sales + Marketing Coordinator
(800) 819-9785
briana@livestories.com
Got Data Doubt?
How to Handle Uncertainty in Data

2. Who am I?
- Attained two Bachelor's degrees from the University of Washington for
Medical Anthropology / Global Health + Evolutionary Biology
- As the sales and marketing coordinator I interact with clients who build resources
around public data regularly.
- Content creator for LiveStatistics resource page: https://www.livestories.com/statistics
(This contains data about your community at a national, state, county, and city level)
Briana Preslar
Sales + Marketing Coordinator
(800) 819-9785
briana@livestories.com

3. Today We Will:
● Learn to identify and handle
- Doubt in the numbers
- Doubt in the analysis
● Identify danger areas in large public data sets
● Address the Margin of Error
The Numbers SEE IT AROUND YOUThe Analysis Doubt in Public Data The Margin of Error

4. The Numbers SEE IT AROUND YOUThe Analysis The Margin of Error Doubt in Public Data
- Doubt in the numbers
- Suppressed data
Learn to identify and handle:
- Doubt in the analysis
- Working with
estimates

5. The Numbers SEE IT AROUND YOUThe Analysis The Margin of Error Doubt in Public Data
- Doubt in the numbers
- Suppressed data
Learn to identify and handle:
- Doubt in the analysis
- Working with
estimates

6. The Numbers The Analysis Doubt in Public DataThe Analysis
Suppressed Data
Areas where data may be suppressed:
- Death data
For Privacy—if # of deaths is > 10 in surveyed location
For Accuracy—if # of deaths is > 20 / 100,000 people
The Numbers The Analysis The Margin of Error Doubt in Public DataThe Analysis

7. The Numbers SEE IT AROUND YOUThe Analysis The Margin of Error Doubt in Public DataThe Analysis
Suppressed Data
Areas where data may be suppressed:
- Death data
For Privacy—if # of deaths is > 10 in surveyed location
For Accuracy—if # of deaths is > 20 / 100,000 people
- Population Data
For Privacy—if you choose to look at too narrow of a segment of people (in
other terms too specific a group) the data may be suppressed.

8. The Numbers The AnalysisThe Analysis
EXAMPLE
- Population Data
For Privacy—if you choose to look at too narrow of a segment of people (in
other terms too specific a group) the data may be suppressed or “not available”
The Numbers SEE IT AROUND YOUThe Analysis The Margin of Error Doubt in Public DataThe Analysis

9. The Numbers SEE IT AROUND YOUThe Analysis The Margin of Error Doubt in Public Data
- Doubt in the numbers
- Suppressed data
Learn to identify and handle:
- Doubt in the analysis
- Working with
estimates

10. The Numbers SEE IT AROUND YOUThe Analysis Doubt in Public DataThe Analysis
- Estimates are used in many public data sets
- Estimates carry doubt with them
Working with Estimates
Doubt in Public Data Doubt in Public DataThe Margin of Error

11. The Numbers SEE IT AROUND YOUThe Analysis Doubt in Public DataThe Analysis
- Estimates are used in many public data sets
- Estimates carry doubt with them
Working with Estimates
SEE IT AROUND YOUDoubt in Public Data SEE IT AROUND YOUDoubt in Public DataThe Margin of Error
WHEN:
NOTE:
When working with 5-
year estimates try NOT
to visualize overlapping
time periods.
1-Year Estimates
● Released annually for
locales with populations
greater than 60,000
● Less granular location
● More granular time
5-Year Estimates
● Used for locales with
populations less than 60,000
● More granular location
● Less granular time

12. The Numbers SEE IT AROUND YOUThe Analysis Doubt in Public DataThe Analysis
- Estimates are used in many public data sets
- Estimates carry doubt with them
Working with Estimates
SEE IT AROUND YOUDoubt in Public Data SEE IT AROUND YOUDoubt in Public DataThe Margin of Error
WHEN:
NOTE:
When working with 5-
year estimates try NOT
to visualize overlapping
time periods.
1-Year Estimates
● Released annually for
locales with populations
greater than 60,000
● Less granular location
● More granular time
5-Year Estimates
● Used for locales with
populations less than 60,000
● More granular location
● Less granular time

13. The Numbers SEE IT AROUND YOUThe Analysis Doubt in Public DataThe Analysis
- Estimates carry doubt with them
SEE IT AROUND YOUDoubt in Public Data SEE IT AROUND YOUDoubt in Public DataThe Margin of Error
The Doubt in Estimates
Count
Total Group Count
When a stat relies on a count, ALWAYS check to see whether the number is a
count or an estimate.
Count
Est. Population*
= Reliable Rate = Unreliable Rate
Numerator
Denominator
= Both values need to be reliable
You rarely find true counts You are often working with estimates
SO

14. The Numbers SEE IT AROUND YOUThe Analysis Doubt in Public DataThe Analysis SEE IT AROUND YOUDoubt in Public Data SEE IT AROUND YOUDoubt in Public DataThe Margin of Error
The Margin of Error—Definition

15. The Numbers SEE IT AROUND YOUThe Analysis Doubt in Public DataThe Analysis
- The Margin of Error is the expected range in which the
answer to your question may be. This is effected by several
variables such as sample size, the confidence interval, and
much more.
SEE IT AROUND YOUDoubt in Public Data SEE IT AROUND YOUDoubt in Public DataThe Margin of Error
The Margin of Error—But really, what is it?

16. The Numbers SEE IT AROUND YOUThe Analysis Doubt in Public DataThe Analysis SEE IT AROUND YOUDoubt in Public Data SEE IT AROUND YOUDoubt in Public DataThe Margin of Error
The Margin of Error—How to interpret itThe Margin of Error—How to interpret it

17. The Numbers SEE IT AROUND YOUThe Analysis Doubt in Public DataThe Analysis SEE IT AROUND YOUDoubt in Public Data SEE IT AROUND YOUDoubt in Public DataThe Margin of Error
The Margin of Error—How to interpret it
Overlapping Margins of Error
- You CANNOT COMPARE two data
points IF their margin of errors are
overlapping.
- BECAUSE the true value could
actually be the same (+ fall in the
overlapping section)

18. The Numbers SEE IT AROUND YOUThe Analysis Doubt in Public DataThe Analysis SEE IT AROUND YOUDoubt in Public Data SEE IT AROUND YOUDoubt in Public DataThe Margin of Error
The Margin of Error—How to interpret itThe Margin of Error—How to interpret it

19. The Numbers SEE IT AROUND YOUThe Analysis Doubt in Public DataThe Analysis SEE IT AROUND YOUDoubt in Public Data SEE IT AROUND YOUDoubt in Public DataThe Margin of Error
The Margin of Error—How to Interpret It

20. The Numbers SEE IT AROUND YOUThe Analysis Doubt in Public DataThe Analysis SEE IT AROUND YOUDoubt in Public Data SEE IT AROUND YOUDoubt in Public DataThe Margin of Error
The Margin of Error—How to Interpret It
VALID!
✓ Small margin of error
✓ No overlapping MOEs
✓ State level data → large count
so it is also considered reliable

21. The Numbers SEE IT AROUND YOUThe Analysis Doubt in Public DataThe Analysis SEE IT AROUND YOUDoubt in Public Data SEE IT AROUND YOUDoubt in Public DataThe Margin of Error
The Margin of Error—How to Interpret It

22. The Numbers SEE IT AROUND YOUThe Analysis Doubt in Public DataThe Analysis SEE IT AROUND YOUDoubt in Public Data SEE IT AROUND YOUDoubt in Public DataThe Margin of Error
The Margin of Error—How to Interpret It
VALID!
✓ Small margin of error
✓ No overlapping MOEs w/ in the
state
✓ No overlapping MOEs w/
national data
✓ State level data → large count
so it is also considered reliable

23. The Numbers SEE IT AROUND YOUThe Analysis Doubt in Public DataThe Analysis SEE IT AROUND YOUDoubt in Public Data SEE IT AROUND YOUDoubt in Public DataThe Margin of Error
The Margin of Error—How to Interpret It
VALID… and invalid
✓ Small margin of error
✓ No overlapping MOEs w/
national data
✓ State level data → large count
so it is also considered reliable
○ Overlapping MOEs w/ in the
state

24. The Numbers SEE IT AROUND YOUThe Analysis Doubt in Public DataThe Analysis SEE IT AROUND YOUDoubt in Public Data SEE IT AROUND YOUDoubt in Public DataThe Margin of Error
The Margin of Error—How to Interpret It
VALID… and invalid
✓ Small margin of error
✓ No overlapping MOEs w/
national data
✓ State level data → large count
so it is also considered reliable
○ Overlapping MOEs w/ in the
state

25. The Numbers SEE IT AROUND YOUThe Analysis Doubt in Public DataThe Analysis SEE IT AROUND YOUDoubt in Public Data SEE IT AROUND YOUDoubt in Public DataThe Margin of Error
The Margin of Error—How to Interpret It
VALID… and invalid
✓ Small margin of error
✓ No overlapping MOEs w/
national data
✓ State level data → large count
so it is also considered reliable
○ Overlapping MOEs w/ in the
state

26. The Numbers SEE IT AROUND YOUThe Analysis Doubt in Public DataThe Analysis SEE IT AROUND YOUDoubt in Public Data SEE IT AROUND YOUDoubt in Public DataThe Margin of Error
The Margin of Error—How to Interpret It
✓ Since we are clearly comparing
the U.S. rates to the national
rates, these are all technically
valid.
○ If we were comparing w/in the
state only one group would be
comparable across groups—the
Asians or Pacific Islander group

27. The Numbers SEE IT AROUND YOUThe Analysis Doubt in Public DataThe Analysis
- Estimates are used in many public data sets
SEE IT AROUND YOUDoubt in Public Data SEE IT AROUND YOUDoubt in Public DataThe Margin of Error
Public Data Estimates
Watch out for ACS group estimates being used in data analysis—and always be
aware of the margin of error in the data. Too large a margin of error will result in
unreliable data or data that is not truly significant.
Rule of Thumb:
- Small Sample Size → High Margin of Error → More unreliable data
- Large Sample Size → Lower Margin of Error → More reliable data

28. The Numbers SEE IT AROUND YOUThe Analysis Doubt in Public DataThe Analysis
- Estimates are used in many public data sets
SEE IT AROUND YOUDoubt in Public Data SEE IT AROUND YOUDoubt in Public DataThe Margin of Error
Public Data Estimates
Watch out for ACS group estimates being used in data analysis—and always be
aware of the margin of error in the data. Too large a margin of error will result in
unreliable data or data that is not truly significant.
Rule of Thumb:
- Small Sample Size → High Margin of Error → More unreliable data
- Large Sample Size → Lower Margin of Error → More reliable data

29. The Numbers SEE IT AROUND YOUThe Analysis Doubt in Public DataThe Analysis
Public Data Doubt: The ACS
SEE IT AROUND YOUDoubt in Public DataThe Margin of Error SEE IT AROUND YOUDoubt in Public Data SEE IT AROUND YOUDoubt in Public DataThe Margin of Error

30. The Numbers SEE IT AROUND YOUThe Analysis Doubt in Public DataThe Analysis SEE IT AROUND YOUDoubt in Public DataThe Margin of Error SEE IT AROUND YOUDoubt in Public Data SEE IT AROUND YOUDoubt in Public DataThe Margin of ErrorThe Numbers SEE IT AROUND YOUThe Analysis Doubt in Public DataThe Analysis
Public Data Doubt: CDC WONDER Data
SEE IT AROUND YOUDoubt in Public DataThe Margin of Error SEE IT AROUND YOUDoubt in Public Data SEE IT AROUND YOUDoubt in Public DataThe Margin of Error

31. Major Takeaways:
- Know where the numbers come from
Surveys, polls, a counting effort, etc.
- Know what the numbers are showing
Counts, estimates, averages, rates, etc.
- Understand the Margin of Error
What it is, how to properly interpret it, how to visualize it.
- Be able to apply this your work with public data
Understand how this applies to data sets like the ACS and WONDER
The Numbers SEE IT AROUND YOUThe Analysis Doubt in Public DataThe Analysis SEE IT AROUND YOUDoubt in Public DataThe Margin of Error SEE IT AROUND YOUDoubt in Public Data SEE IT AROUND YOUDoubt in Public DataThe Margin of Error

32. Resources We Find Helpful
The United States Census Bureau - Home Page
The United States Census Bureau - Data Suppression
Page
CDC WONDER FAQ - Includes answers on
suppressed numbers
Margin of Error Definitions are linked to images on
slides
See Data About Your Community:
LiveStories.com/Statistics
The Numbers SEE IT AROUND YOUThe Analysis Doubt in Public DataThe Analysis SEE IT AROUND YOUDoubt in Public DataThe Margin of Error SEE IT AROUND YOUDoubt in Public Data SEE IT AROUND YOUDoubt in Public DataThe Margin of Error

33. LiveStories Resources
See Data About Your Community:
LiveStories.com/Statistics
The Numbers SEE IT AROUND YOUThe Analysis Doubt in Public DataThe Analysis SEE IT AROUND YOUDoubt in Public DataThe Margin of Error SEE IT AROUND YOUDoubt in Public Data SEE IT AROUND YOUDoubt in Public DataThe Margin of Error

34. The Numbers SEE IT AROUND YOUThe Analysis Doubt in Public DataThe Analysis SEE IT AROUND YOUDoubt in Public DataThe Margin of Error SEE IT AROUND YOUDoubt in Public Data SEE IT AROUND YOUDoubt in Public DataThe Margin of Error
LiveStories Resources
See Data About Your Community:
LiveStories.com/Statistics

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Briana Preslar
Sales + Marketing Coordinator
(800)819-9785
briana@livestories.com