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Chapter 3

# L33 Psych 300 Chapter Notes - Chapter 3: Misleading Graph, Pareto Chart, Scatter Plot

Department
Psychological & Brain Sci (Psychology)
Course Code
L33 Psych 300
Professor
Nestojko
Chapter
3

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Chapter 03: Visual Displays of Data
Beware of misleading graphs! Here are some ways graphs can be misleading:
1. Biased Scale Lie
a. Food magazine rating restaurants, but the ones with zero are just the ones that
the magazine reviewers have not been to, this is not an accurate scale
2. Sneaky Sample Lie
a. The people that rate professors on those websites most likely really loved or really
hated the professor
3. Interpolation Lie
a. Assuming that some value between the data points lies on a straight line between
those data points
b. Ex: reporting a drop in crime between two years without looking at the other
years before or afterward
4. Extrapolation Lie
a. Assuming that values beyond the data points will continue indefinitely
5. Inaccurate Values Lie
a. Tells the truth in one part of the data but visually distorts it in another place
b. Truncated Y-Values: when you “zoom in” and make the difference look huge on
bar graphs and in reality it is just .01% or less
Types of Graphs:
Scatter Plot Graph - a graph that depicts the relation between two scale variables
No Relation: looks like a jumble of random dots
Linear Relation: the relation between variables is best described by a straight line
Nonlinear Relation: the relation between variables is best described by a line that
breaks or curves in some way
Line Graph - a graph that is used to illustrate the relation between two scale variables
Time Plot: a graph that plots a scale variable on the y-axis as it changes over an
increment of time labeled on the x-axis
Bar Graph - a visual depiction of data in which the independent variable is nominal or
ordinal and the dependent variable is scale
Great for highlighting differences in means
Independent Variable (X - axis): Nominal or Ordinal
Dependent Variable (Y - axis): Scale
Describe the data as (what you are measuring) on the (test name) test
The height of each bar typically represents the average value of the dependent
variable for each category