Misleading Graphs

4 Key excerpts on "Misleading Graphs"

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  eBook - PDF

  Seeing Through Statistics

  • Jessica Utts(Author)
  • 2014(Publication Date)
  • Cengage Learning EMEA

    (Publisher)

  • There are numerous ways that pictures of data can convey misleading messages. Make sure you carefully examine graphs and plots and don’t be fooled by distorted visual displays of information. • Scatter plots are useful for displaying the relationship between two measurement variables and can reveal outliers that are not apparent from pictures of each vari-able on its own. • A perfectly good graph can convey a misleading message if the information used to create it is not thoughtfully presented. For instance, prices across time should be adjusted for inflation, and measurements for groups of individuals across time should be presented as numbers per capita instead of as totals for the whole group. • Time series plots present measurements across regularly spaced intervals. They can show trends, seasonal patterns, and cycles, but can also be distorted to convey a misleading message—for instance, by showing a short time period. Exercises Exercises with numbers divisible by 3 (3, 6, 9, etc.) are included in the Solutions at the back of the book. They are marked with an asterisk (*) . 1. Use the pie charts in Figure 9.1 to create a bar graph comparing eye colors for Caucasian boys and Caucasian girls. 2. Suppose a real estate company in your area sold 100 houses last month, whereas their two major competitors sold 50 houses and 25 houses, respectively. The top company wants to display its bet-ter record with a pictogram using a simple two-dimensional picture of a house. Draw two pictograms displaying this information, one of which is misleading and one of which is not. (The horizontal axis should list the three companies and the vertical axis should list the number of houses sold.) *3. Give the name of a type of statisti-cal picture that could be used for each of the following kinds of data: *a. One categorical variable *b. One measurement variable *c. Two categorical variables *d. Two measurement variables 4.

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  Mathematical Discourse

  Language, Symbolism and Visual Images

  • Kay O'Halloran(Author)
  • 2004(Publication Date)
  • Continuum

    (Publisher)

  Misleading Graphs are those which present information in such a fashion that an unwarranted Prominence is attached to particular dimensions of the display. Prominence is achieved through specific choices for interpersonal, experiential and/or composition meaning. The visual display of information in fields such as advertising, newspaper discourse, politics and so forth is worthy of further investigation. From the analysis of choice from systems for interpersonal meaning in Plate 5.2(1), three features of mathematical visual images become evident. First, the viewer is explicitly directed to the relevant parts of the visual display through the nature of the interpersonal choices at each rank. Inter-personal meaning is not a delicate balance of a variety of unobtrusive strat-egies working in harmony as for example, in O'Toole's (1994) descriptions of the Primavera. The important Episodes, Figures and Parts are explicitly marked through choices for Labels, Position, Colour, Framing, Line Width, Line Solidarity and so forth. Second, the Modality or the truth-value of the visual image is high, but this is not because mathematical visual images faithfully depict material 'reality'. While mathematical visual images relate to our perceptual under-standing of reality, there is not the same degree of correspondence as portrayed in photographs, for example. Rather the high level of certainty attached to mathematical and scientific representations has been culturally assigned: 'visual modality rests on culturally and historically determined standards of what is real and what is not, and not on the objective cor-respondence of the visual image to a reality defined independently of it' (Kress and van Leeuwen, 1996: 168). The perfection and exactness of the visual displays, the lack of contextual information, the Style of Production and the metaphorical and abstract nature of the Episodes, Figures and

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  eBook - ePub

  Statistical Literacy at School

  Growth and Goals

  • Jane M. Watson(Author)
  • 2013(Publication Date)
  • Routledge

    (Publisher)

  3Graphs—How Best to Represent the Data

  For many people the first word that comes to mind when they think about statistical charts is “lie.” No doubt some graphics do distort the underlying data, making it hard for the viewer to learn the truth. But data graphs are no different from words in this regard, for any means of communication can be used to deceive. There is no reason to believe that graphs are especially vulnerable to exploitation by liars; in fact, most of us have pretty good graphical lie detectors that help us see right through frauds.

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  3.1 Background

  Edward Tufte’s assessment of graphs as similar to words as a means of communication with no more or less potential to deceive is an important issue in considering the place of data representation in the data and chance curriculum. His view that most people have “pretty good graphical lie detectors,” however, is not borne out at the school level. It is there that a conscious effort needs to be expended to achieve the skills that will provide the lie detectors required by statistically literate adults.

  In contrast to sampling, graphing of data has a firm and explicit place in the data and chance curriculum. Even before the mathematics curriculum was expanded to contain coverage of data handling generally, many textbooks included exercises in graphing, leading for example to histograms. It is interesting that the link to algebraic graphs was often exceedingly tenuous although both types of graphs rely on a coordinate system. Although early in the last century some analytic geometry texts introduced the idea of “empirical curves,”2 only very recently have curriculum materials again sought to establish meaningful links between graphs of data sets and the graphs of equations that might describe them more formally.3

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  Data Visualization

  Exploring and Explaining with Data

  • Jeffrey Camm, James Cochran, Michael Fry, Jeffrey Ohlmann(Authors)
  • 2021(Publication Date)
  • Cengage Learning EMEA

    (Publisher)

  As shown in the Data Visualization Makeover for this chapter, considerations as simple as not adjusting for inflation can completely change the resulting data visualization and the insights drawn from the visualization. The issues covered in this chapter will help create data visualizations that are clear and truthful for the audience. All data visualizations begin with data. If we use wrong, incomplete, or biased data, then our visualization will not tell the complete truth. Therefore, we begin this chapter by talking about common issues with data, such as missing data and biased data, that can cause misleading or confusing data visualizations. We discuss deceptive data visualization designs that should be avoided when creating visualizations. These deceptive designs often involve improper choices related to the design of chart axes or choices on which data to include in the visualization. We also discuss issues that can arise when creating geographic maps, and we provide recommendations for how to avoid misleading charts when creating these types of maps. 9-1 Missing Data and Data Errors Many real-life data sets suffer from missing data values and data errors. There are many reasons why data sets may contain missing values and data errors, and it is important to understand the reasons behind the missing values in order to know how they should be treated. Removing or replacing missing data values and errors in data affects all subsequent analysis of that data, including the creation of data visualizations. In this section, we exam- ine some common methods of identifying missing data and data errors, and we discuss methods for dealing with missing data and data errors. Identifying Missing Data Consider the case of Blakely Tires, a producer of automobile tires located in the United States.

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