Data Visualization,
2e
Chapter 9: Telling the Truth with
Data Visualization
Camm, Cochran, Fry & Ohlmann, Data Visualization- Exploring and Explaining with Data, 2nd Edition. © 2025 Cengage. All Rights Reserved.
May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
1
Chapter Objectives (1 of 2)
After completing this chapter, you will be able to:
LO 9.1
Identify missing data and data errors using Excel.
LO 9.2
Define the meaning of biased data and explain the concepts of
selection bias and survivor bias.
LO 9.3
Define Simpson’s paradox and explain how a scatter chart can be
used to identify some instances of Simpson’s paradox.
LO 9.4
Explain the importance of adjusting for inflation in time series data
that represent long time periods and use a price index to adjust
nominal values to account for inflation.
Camm, Cochran, Fry & Ohlmann, Data Visualization- Exploring and Explaining with Data, 2nd Edition. © 2025 Cengage. All Rights Reserved.
May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
2
Chapter Objectives (2 of 2)
LO 9.5
Identify deceptive design practices related to the axes used in charts and
suggest ways to improve the axes in these charts to communicate insights
to the audience more clearly.
LO 9.6
Explain why dual-axis charts are often confusing and misleading for
audiences and suggest an alternative to using a dual-axis chart that is less
confusing for the audience.
LO 9.7
Explain how the range of data and the temporal frequency of data
included in a chart affects the insights conveyed to the audience.
LO 9.8
Explain why some geographic maps can result in misleading data
visualizations and provide recommendations for how to improve these
types of maps.
Camm, Cochran, Fry & Ohlmann, Data Visualization- Exploring and Explaining with Data, 2nd Edition. © 2025 Cengage. All Rights Reserved.
May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
3
Data Visualization Makeover
Scatter charts showing the effect of inflation adjustment on gross revenues for the
lifetime North America box office top-10 movies of all time
In 1951, the average price of a movie ticket was $0.53. In 2020, it was $9.37, or 17.7 times more
expensive.
Source: https://help.imdb.com/article/imdbpro/industry-research/box-office-mojo-by-imdbprofaq/GCWTV4MQKGWRAUAP?ref_=mojo_cso_md#inflation.
Camm, Cochran, Fry & Ohlmann, Data Visualization- Exploring and Explaining with Data, 2nd Edition. © 2025 Cengage. All Rights Reserved.
May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
4
9.1 Missing Data and Data Errors
A portion of an Excel spreadsheet showing Blakely Tires data
Data file: BlakelyTires
Data set: it consists of information about
the four tires from 116 automobiles.
Company: Blakely Tires is a producer of
automobile tires located in the U.S.
Issue: assess data quality.
Study objective: to learn about the conditions of its tires on automobiles in Texas.
Reported variables: tire position, age, tread depth, and mileage.
Camm, Cochran, Fry & Ohlmann, Data Visualization- Exploring and Explaining with Data, 2nd Edition. © 2025 Cengage. All Rights Reserved.
May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
5
9.1 Identifying Missing Data by Counting Data
Blanks
A portion of an Excel spreadsheet showing number of missing values for variables
in Blakely Tires data
To count the missing observations for
each variable in the file BlakelyTires,
follow the step-by-step instructions
included in the notes.
Camm, Cochran, Fry & Ohlmann, Data Visualization- Exploring and Explaining with Data, 2nd Edition. © 2025 Cengage. All Rights Reserved.
May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
6
9.1 Identifying Missing Data Using
Conditional Formatting
A portion of an Excel spreadsheet showing Blakely Tires data sorted from lowest
to highest by ID number and missing value highlighted
To highlight the missing observations for each
variable in the file BlakelyTires, using the Excel
Conditional Formatting tool, follow the step-bystep instructions included in the notes.
Camm, Cochran, Fry & Ohlmann, Data Visualization- Exploring and Explaining with Data, 2nd Edition. © 2025 Cengage. All Rights Reserved.
May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
7
9.1 Identifying Extreme Data Errors
A portion of an Excel spreadsheet showing Blakely
Tires data sorted on Life of Tires (Months) and
calculated summary statistics
We can use summary statistics to
detect suspicious values.
• Life of Tire: a maximum value of 601
months for a left rear tire. Comparison
with the other tires suggests a value of
60.1 months instead.
• Tread Depth: a minimum value of
0.0/12ths (undrivable) and a maximum
value of 16.7/12ths, which exceeds
tread depth on new tires.
*See notes for step-by-step instructions
on how to calculate summary statistics.
Camm, Cochran, Fry & Ohlmann, Data Visualization- Exploring and Explaining with Data, 2nd Edition. © 2025 Cengage. All Rights Reserved.
May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
8
9.1 Identifying Non-Extreme Data Errors
Scatter chart for the Blakely Tires data
used to identify possible data errors
Not all erroneous values in a data set are
extreme.
To identify non-extreme erroneous values, we
can investigate relationships between
quantitative variables using data visualization
tools such as scatter charts.
Here, tire mileage has a strong negative
relationship with tread depth because the tire
tread slowly erodes while driving.
See the notes for additional information about
identifying potential non-extreme data errors in
the data set for further analysis by exploring data
points lying outside of the expected red ellipse.
Camm, Cochran, Fry & Ohlmann, Data Visualization- Exploring and Explaining with Data, 2nd Edition. © 2025 Cengage. All Rights Reserved.
May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
9
9.2 Biased Data
Biased data occurs when representing an intended population by drawing data
from a non-random sample. There are two many types of bias:
• Selection bias occurs when the data drawn from a non-random sample to represent
the intended population.
• Example of selection bias: political polling conducted on landline phone numbers may
result in a sample biased in terms of age of the respondents.
• Simpson’s paradox is a type of selection bias. It occurs when a specific trend that appears
in subsets of data disappears or reverses when the subsets are aggregated.
• Survivor bias occurs when sample data consists of a disproportionately large number
of observations corresponding to outcomes for a particular event.
Camm, Cochran, Fry & Ohlmann, Data Visualization- Exploring and Explaining with Data, 2nd Edition. © 2025 Cengage. All Rights Reserved.
May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
10
9.2 Example of Simpson’s Paradox
A data portion used to examine the relationship between age and income
Data file: AgeIncome
Study objective: to analyze factors that affect annual incomes
in the U.S.
Reported variables: age, income, and location (Dallas, San
Francisco, and Naples) for 106 respondents.
Bias issue: the three locations are not necessarily
representative of the U.S. population.
Approach: build a scatter chart of the data aggregated by
location and compare it to scatter charts in which the data were
disaggregated by location.
Camm, Cochran, Fry & Ohlmann, Data Visualization- Exploring and Explaining with Data, 2nd Edition. © 2025 Cengage. All Rights Reserved.
May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
11
9.2 Visualization of Simpson’s Paradox
Scatter charts illustrating how Simpson’s paradox may lead to erroneous conclusions
when considering the relationship between age and income on aggregated data
Simpson’s paradox: the
trend that appears in the
aggregated data reverses
when the locations are
plotted separately.
Here, age and income display
• a negative relationship when
aggregated.
• a positive relationships when
disaggregated by location.
Camm, Cochran, Fry & Ohlmann, Data Visualization- Exploring and Explaining with Data, 2nd Edition. © 2025 Cengage. All Rights Reserved.
May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
12
9.2 Origin of Survivor Bias
Depiction of common areas of damage to Just as for selection bias, with survivor bias, the
sample data are not represent-tative of the
returning planes in World War II
population under study.
Examined data on surviving World War II planes
showed consistent damage to the indicated
areas (X).
The initial suggested action was to armor tails
and wings.
However, the mathematician Abraham Wald
successfully argued that the correct action was
the opposite: because damage to tails and wings
were least harmful to the aircraft, armor should
be added instead to areas not showing damage.
Camm, Cochran, Fry & Ohlmann, Data Visualization- Exploring and Explaining with Data, 2nd Edition. © 2025 Cengage. All Rights Reserved.
May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
13
9.2 Example of Survivor Bias
Comparing average risk tolerance of entrepreneurs to a control group of nonentrepreneurs
Research hypothesis: entrepreneurs are
more likely to have a greater risk tolerance
than non-entrepreneurs.
Data set: results of a survey that measures
the tolerance to take on financial risk for 87
entrepreneurs, compared to a control
group of 100 randomly selected nonentrepreneurs.
The conclusions are affected by survival
bias, as the only data we have on
entrepreneurs is successful ones.
Camm, Cochran, Fry & Ohlmann, Data Visualization- Exploring and Explaining with Data, 2nd Edition. © 2025 Cengage. All Rights Reserved.
May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
14
9.3 Adjusting for Inflation: Example
A portion of data with nominal
prices for a gallon of gasoline and
price index in the United States
between 1978 and 2017
A price index is the aggregated price of a basket of
products and services.
Inflation refers to the general increase in nominal
prices over time for the price index.
Data file: PriceGasoline
Study objective: to analyze the effect of inflation on
gas prices between 1978 and 2017.
Reported variables: nominal gas price per gallon
and price index.
Calculated variable: inflation-adjusted gas price per
gallon.
Camm, Cochran, Fry & Ohlmann, Data Visualization- Exploring and Explaining with Data, 2nd Edition. © 2025 Cengage. All Rights Reserved.
May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
15
9.3 Adjusting for Inflation: Calculations
Adjusting Nominal gasoline prices for Inflation in Excel
Inflation-adjusted prices are calculated for a base year. In this
example, we used 2017:
Infl.adjusted G as Price 1978
Price Index 2017
 G as Price 1978 
Price Index 1978
211.8
Infl.adjusted G as Price 1978  $0.65 
 $2.66
51.9
In cell D2 of the Excel
spreadsheet, we can implement
the above equation as:
=B2*($C$41/C2)
And paste it to cells D3:D41.
Camm, Cochran, Fry & Ohlmann, Data Visualization- Exploring and Explaining with Data, 2nd Edition. © 2025 Cengage. All Rights Reserved.
May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
16
9.3 Adjusting for Inflation: Visualization
The inflation-adjusted price per gallon of gasoline in the United States has
decreased between 1978 and 2017
We can adjust nominal values to account
for inflation using any year in the time
series.
The selection of the base year generally
depends on the comparisons to be made
Camm, Cochran, Fry & Ohlmann, Data Visualization- Exploring and Explaining with Data, 2nd Edition. © 2025 Cengage. All Rights Reserved.
May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
17
9.4 Improper Chart Axes: Exaggerated Difference
A column chart that exaggerates the
difference in the proportion of likely voters
Redesigned column chart showing just a
small difference in the proportion of
likely voters
Camm, Cochran, Fry & Ohlmann, Data Visualization- Exploring and Explaining with Data, 2nd Edition. © 2025 Cengage. All Rights Reserved.
May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
18
9.4 Improper Chart Axes: Hidden Insight
A line chart with a wide range showing
no increase in the earth’s surface
temperature
A line chart with a revised range showing
an increase in the earth's surface
temperature
*See notes for additional information on deceptive chart designs.
Camm, Cochran, Fry & Ohlmann, Data Visualization- Exploring and Explaining with Data, 2nd Edition. © 2025 Cengage. All Rights Reserved.
May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
19
9.4 Improper Chart Axes: Incorrect Aspect Ratio
Two different line charts for the average annual global surface temperature data showing
the effect of line chart aspect ratio on the insights conveyed to the audience
The aspect ratio refers to the width to height ratio of a chart.
The line chart in (a), tall but not very wide, has a smaller aspect ratio
than the line chart in (b), which is short but wide.
*See notes for additional information on the aspect ratio of charts.
Camm, Cochran, Fry & Ohlmann, Data Visualization- Exploring and Explaining with Data, 2nd Edition. © 2025 Cengage. All Rights Reserved.
May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
20
9.4 Dual-Axis Charts: Deceptive Design
Effect of using a dual-axis chart to display the gross domestic product and unemployment
rate in the United States since 2000
A dual-axis chart uses a secondary axis to represent one of the variables to show on the same
chart the different vertical ranges.
Dual-axis charts may be difficult to interpret and may mislead the audience, such as by causing to
erroneously interpret the points in which the lines appear to intersect.
Camm, Cochran, Fry & Ohlmann, Data Visualization- Exploring and Explaining with Data, 2nd Edition. © 2025 Cengage. All Rights Reserved.
May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
21
9.4 Dual-Axis Charts: Remedy
Using two different charts to display the gross domestic product and
unemployment rate in the United States since 2000
A simpler alternative to a dual-axis chart consists of using two separate charts with each variable
shown individually.
*See notes for instructions on how to build dual-axis charts.
Camm, Cochran, Fry & Ohlmann, Data Visualization- Exploring and Explaining with Data, 2nd Edition. © 2025 Cengage. All Rights Reserved.
May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
22
9.4 Data Selection and Temporal Frequency
Comparing stock prices with the date range but different temporal frequencies
The temporal frequency refers to the rate at which time-series data are plotted:
a) Share price plotted with a daily temporal frequency shows peaks and valleys.
b) Share price plotted with a bi-monthly temporal frequency appears smoothed.
Viewing time series data at different temporal frequencies can reveal different patterns.
Camm, Cochran, Fry & Ohlmann, Data Visualization- Exploring and Explaining with Data, 2nd Edition. © 2025 Cengage. All Rights Reserved.
May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
23
9.4 Issues Related to Geographic Maps
Choropleth map showing number of
people living in poverty by state
Choropleth map showing poverty rate by
state
The poverty rate by state is a better choropleth map for communicating which states have the
highest number of people living in poverty relative to the overall state population.
*See notes on how to calculate poverty rate by state and adding data labels to maps.
Camm, Cochran, Fry & Ohlmann, Data Visualization- Exploring and Explaining with Data, 2nd Edition. © 2025 Cengage. All Rights Reserved.
May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
24
Discussion Activity
• Consider these three studies:
1. In the 1930s, Dr. Joseph Rhine tested 500 participants to guess the order in a shuffled deck of cards.
Only those participants who guessed correctly moved to the next round. The last participant to guess
right every round was declared to have telepathic abilities.
2. A policy group lobbying for the alcohol industry conducted an exit poll outside of a popular restaurant
late at night. The restaurant patrons were asked whether driving after drinking was a serious problem.
The policy group concluded that driving after drinking was not a serious problem.
3. A research group followed for 40 years a cohort of individuals. The study’s main conclusion was that
those who drank red wine were richer, better educated, had lower levels of cholesterol ratio, and
consequently a lower risk of heart attacks than those who did not.
• Associate each of the three studies to one of the following forms of bias:
a. Selection bias
b. Simpson’s Paradox
c. Survivor Bias
Camm, Cochran, Fry & Ohlmann, Data Visualization- Exploring and Explaining with Data, 2nd Edition. © 2025 Cengage. All Rights Reserved.
May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
25
Check Your Knowledge
• Due to their cause and effect on the insights drawn from data, outliers should __________.
a.
only be removed after careful consideration
b.
immediately be removed for accurate analysis
c.
never be removed
d.
be replaced with incorrect data
• Which of these statements describes how survivor bias and selection bias are related?
a.
The sample data set consists of a disproportionately large number of observations corresponding to
positive outcomes for a particular event.
b.
The sample data are not representative of the population that is being studied.
c.
The sample has not been properly randomized to represent the intended population.
d.
They are not related in any way.
Camm, Cochran, Fry & Ohlmann, Data Visualization- Exploring and Explaining with Data, 2nd Edition. © 2025 Cengage. All Rights Reserved.
May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
26
Summary
In this chapter, you should have learned how:
• To identify missing data and data errors using different Excel methods that depend on the nature
of the type of data and problem settings being analyzed.
• Biased data may mislead insight from data visualization and how to identify it.
• To adjust for inflation when dealing with time series data that have been collected over long time
periods.
• Different ranges of values for the axes can greatly affect the insights conveyed to the audience
for the same data.
• To remedy dual-axis charts that may often be misleading for the audience.
• The selection and frequency of time series data can change the insights conveyed to the
audience.
• To prevent misleading the audience when using geographic maps.
Camm, Cochran, Fry & Ohlmann, Data Visualization- Exploring and Explaining with Data, 2nd Edition. © 2025 Cengage. All Rights Reserved.
May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
27