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Common Mistakes in Graphics

Common Mistakes in Graphics. Excess information Multiple scales Using symbols in place of text Poor scales Using lines incorrectly. Excess Information. Sneaky trick to meet length limits Rules of thumb: 6 curves on line chart 10 bars on bar chart 8 slices on pie chart

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Common Mistakes in Graphics

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  1. Common Mistakes in Graphics • Excess information • Multiple scales • Using symbols in place of text • Poor scales • Using lines incorrectly

  2. Excess Information • Sneaky trick to meet length limits • Rules of thumb: • 6 curves on line chart • 10 bars on bar chart • 8 slices on pie chart • Extract essence, don’t cram things in

  3. Way Too Much Information

  4. What’s ImportantAbout That Chart? • Times for cp and rcp rise with number of replicas • Most other benchmarks are near constant • Exactly constant for rm

  5. The Right Amountof Information

  6. True Confessions

  7. Multiple Scales • Another way to meet length limits • Basically, two graphs overlaid on each other • Confuses reader (which line goes with which scale?) • Misstates relationships • Implies equality of magnitude that doesn’t exist

  8. Some Especially Bad Multiple Scales

  9. Using Symbolsin Place of Text • Graphics should be self-explanatory • Remember that the graphs often draw the reader in • So use explanatory text, not symbols • This means no Greek letters! • Unless your conference is in Athens...

  10. It’s All Greek To Me...

  11. Explanation is Easy

  12. Poor Scales • Plotting programs love non-zero origins • But people are used to zero • Fiddle with axis ranges (and logarithms) to get your message across • But don’t lie or cheat • Sometimes trimming off high ends makes things clearer • Brings out low-end detail

  13. Nonzero Origins(Chosen by Microsoft)

  14. Proper Origins

  15. A Poor Axis Range

  16. A Logarithmic Range

  17. A Truncated Range

  18. Using Lines Incorrectly • Don’t connect points unless interpolation is meaningful • Don’t smooth lines that are based on samples • Exception: fitted non-linear curves

  19. Incorrect Line Usage

  20. Pictorial Games • Non-zero origins and broken scales • Double-whammy graphs • Omitting confidence intervals • Scaling by height, not area • Poor histogram cell size

  21. Non-Zero Originsand Broken Scales • People expect (0,0) origins • Subconsciously • So non-zero origins are a great way to lie • More common than not in popular press • Also very common to cheat by omitting part of scale • “Really, Your Honor, I included (0,0)”

  22. Non-Zero Origins

  23. The Three-Quarters Rule • Highest point should be 3/4 of scale or more

  24. Double-Whammy Graphs • Put two related measures on same graph • One is (almost) function of other • Hits reader twice with same information • And thus overstates impact

  25. OmittingConfidence Intervals • Statistical data is inherently fuzzy • But means appear precise • Giving confidence intervals can make it clear there’s no real difference • So liars and fools leave them out

  26. Graph WithoutConfidence Intervals

  27. Graph WithConfidence Intervals

  28. Confidence Intervals • Sample mean value is only an estimate of the true population mean • Bounds c1 and c2 such that there is a high probability, 1-a, that the population mean is in the interval (c1,c2): Prob{ c1 < m < c2} =1-awhere a is the significance level and100(1-a) is the confidence level • Overlapping confidence intervals is interpreted as “not statistically different”

  29. Graph WithConfidence Intervals

  30. Reporting Only One Run(tell-tale sign) Probably a fluke(It’s likely that withmultiple trials this would go away)

  31. 1960 1980 Scaling by HeightInstead of Area • Clip art is popular with illustrators: Women in the Workforce

  32. The Troublewith Height Scaling • Previous graph had heights of 2:1 • But people perceive areas, not heights • So areas should be what’s proportional to data • Tufte defines a lie factor: size of effect in graphic divided by size of effect in data • Not limited to area scaling • But especially insidious there (quadratic effect)

  33. 1960 1980 Scaling by Area • Here’s the same graph with 2:1 area: Women in the Workforce

  34. Histogram Cell Size • Picking bucket size is always a problem • Prefer 5 or more observations per bucket • Choice of bucket size can affect results:

  35. Histogram Cell Size • Picking bucket size is always a problem • Prefer 5 or more observations per bucket • Choice of bucket size can affect results:

  36. Histogram Cell Size • Picking bucket size is always a problem • Prefer 5 or more observations per bucket • Choice of bucket size can affect results:

  37. Don’t Quote DataOut of Context

  38. The Same Data in Context

  39. Tell the Whole Truth

  40. Tell the Whole Truth

  41. Special-Purpose Charts • Histograms • Scatter plots • Gantt charts • Kiviat graphs

  42. Tukey’s Box Plot • Shows range, median, quartiles all in one: • Variations: minimum quartile median quartile maximum

  43. Histograms

  44. Scatter Plots • Useful in statistical analysis • Also excellent for huge quantities of data • Can show patterns otherwise invisible

  45. Gantt Charts • Shows relative duration of Boolean conditions • Arranged to make lines continuous • Each level after first follows FTTF pattern

  46. Gantt Charts • Shows relative duration of Boolean conditions • Arranged to make lines continuous • Each level after first follows FTTF pattern F T F T T F F T T F F T T F

  47. Kiviat Graphs • Also called “star charts” or “radar plots” • Useful for looking at balance between HB and LB metrics HB LB

  48. Useful Reference Works • Edward R. Tufte, The Visual Display of Quantitative Information, Graphics Press, Cheshire, Connecticut, 1983. • Edward R. Tufte, Envisioning Information, Graphics Press, Cheshire, Connecticut, 1990. • Edward R. Tufte, Visual Explanations, Graphics Press, Cheshire, Connecticut, 1997. • Darrell Huff, How to Lie With Statistics, W.W. Norton & Co., New York, 1954

  49. Ratio Games • Choosing a Base System • Using Ratio Metrics • Relative Performance Enhancement • Ratio Games with Percentages • Strategies for Winning a Ratio Game • Correct Analysis of Ratios

  50. Choosing a Base System • Run workloads on two systems • Normalize performance to chosen system • Take average of ratios • Presto: you control what’s best

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