Statistics can be used to describe patterns in data but can also be misused or misinterpreted. There are different types of statistics including measures of central tendency, measures of spread, and probability. Graphs are useful for visualizing data but can be misleading without proper context or labels. Advanced statistical software packages and online resources make powerful data visualization and sharing of insights possible. Proper statistical reasoning requires considering multiple perspectives and being transparent about any limitations in the data or analysis.

1. Or You Can Lie With Statistics but it’s a Lot Easier with
Words

2. Everything is Numbers
 Statistics are used to estimate & describe patterns in
nature that aren’t easy to see with the naked eye
 Sports-Earned Run Average, Slugging Percentage, QB
Rating, Goals Against Average
 Economics-Gross Domestic Product, Unemployment,
Inflation
 Medicine-Heart Rate, % Body Fat, T-Cell Counts
 Education-IQ Scores, SAT scores, Dropout Rates
 As long the statistic is from a source of data that is
verifiable, it’s hard to lie using it.

3. Types of Statistics
 Measures of Central Tendency (aka Averages)
 Continuous-Number can take any value.
 Mean (sum of all data divided by the number of data points)
 Median (midpoint of all data when it is ranked from highest to
lowest)
 Mode (most frequently occurring data value)
 Discrete-Value can only take certain values eg. 0 or 1,
true or false.
 Proportion-sum of values taking a certain value for a given
variables divided by the maximum value for that variable.

4. Types of Statistics (cont.)
 Measures of Spread
 Range-highest data value-lowest data value
 Variance-Average squared deviation from the mean
 Standard Deviation-square root of the variance
 Probability
 Used to measure the chance of events
 Also used to make a statement about the relationship
between a sample and a population that it’s taken from
eg. margin of error.

5. But a Summary Statistic can Never
Tell You the Whole Story
Graph with States Graph without States

6. Graph Types
Bar Graph-Good visually but not Line Graph-Better for showing
good for trends trends over time
6
time 4
5
4
time 3
Product A 3 Product A
Product B 2 Product B
time 2
Product C Product C
1
time 1 0
time time time time
0 10 20 1 2 3 4

7. Graph Types (Cont)
This is the first pie chart created by
Florence Nightingale to show the
number of British soldiers in the
Crimean War who died due to
infection rather than combat
injuries.

8. Graph Types (cont.)
Mapping using Geographical
Information Systems (GIS) is a
good way to represent data by
region. In this graph I showed
which areas of the city have the
highest number of crimes by
census tract in the city for 2005.

9. Posting Graphs on the Web
 Line, Bar, Pie, & other Graphs can be created using
Microsoft Excel, SPSS, SAS, ArcGIS, R, & other
Packages
 If that data package will allow you to save that graph as
a .jpg, .gif, or .png file you can easily add it to your
blog.
 Microsoft Excel requires a visual basic command to save
graphs as image files.

10. Statistical Packages
 Microsoft Excel-Most readily available but not really
built for statistical analysis. OK to make basic graphs.
 SPSS-Better for more advanced analysis and graphics
but less accessible due to cost. User friendly.
 R-Free software package that can be downloaded from
the web. Can do many types of analyses. BUT it is
syntax driven. Can save graphics as image files using
syntax.

11. Cutting Edge Graphics
 The Gapminder institute provides great interactive
graphics for free that can be seen in the documentary the
Joy of Stats.
 URL: www.gapminder.org
 Joy of Stats Clip:
http://csiwodeadbodies.blogspot.com/2010/12/income-and-
life-expectancy-what-does-it.html
 The website Fractracker uses advanced graphics and
mapping techniques to monitor the impacts of Marcellus
Shale drilling in Pennsylvania and New York.
 URL: http://www.fractracker.org/

12. Poor Statistical Reasoning Example
 The blog The Audacious Epigone posted an analysis of
the IQ’s of a sample of McCain & Obama voters which
can be seen at
http://anepigone.blogspot.com/2011/05/iq-wars-
mccains-voters-win.html

13. Some Good Statistical Blogs
 FiveThirtyEight-Nate Silver’s blog which forecasts
elections, the Oscars, and other sporting events.
http://fivethirtyeight.blogs.nytimes.com/
 Data Visualisation-Has more examples of cutting edge
graphics. http://www.datavis.ca/
 The Incidental Economist-Good Analysis of health
care data.
http://theincidentaleconomist.com/wordpress/
 CSI without Dead Bodies-My own website
http://csiwodeadbodies.blogspot.com

14. Sources of Data on the Web
 Many websites, such as The Census Bureau’s provide
data for download with which to do your own analysis.
 Example-Small Area Health Insurance Estimates
(SAHIE) makes state and county level estimates for the
whole US from 2005-2007 (2008 and 2009 estimates are
forthcoming)
http://www.census.gov/did/www/sahie/index.html
 Other sites provide data that can be copied and pasted
into a data file.
 Example-CNN makes it’s poll reports available as PDF’s
but not the raw data

15. Summary
 When analyzing data leave no stones unturned
 or if that is impossible turn over as many as possible and
acknowledge that you couldn’t turn all of them over.
 When interpreting an analysis ask yourself if they have
turned over the important stones and or accounted for
the ones that they couldn’t turnover.