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SEEING IS BELIEVING: Telling stories with statistics – in pictures

SEEING IS BELIEVING: Telling stories with statistics – in pictures. We’re failing. Do you see the same thing here?. This is your brain on statistics. The total sample is (roughly) evenly divided by gender.

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SEEING IS BELIEVING: Telling stories with statistics – in pictures

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  1. SEEING IS BELIEVING: Telling stories with statistics – in pictures

  2. We’re failing

  3. Do you see the same thing here?

  4. This is your brain on statistics

  5. The total sample is (roughly) evenly divided by gender. Subtracting 72 from the 150 one would expect gives a value of about 80, which squared is 6,400. It is already obvious this is significant.

  6. Just for closure ..

  7. Seeing is a learned skill Statisticians may see things in a picture others don’t

  8. My points (surprisingly, I do have some)

  9. Data Visualization Graphics do not necessarily stand alone

  10. Data visualization is all around us.

  11. Visual representation in one context is often misapplied to another.

  12. Atomic numbers on your socks?

  13. Data visualization needs to ADD information

  14. Basic Assumptions • Our audience needs to be taught to read visual data just as we read numeric data, and we need to learn to have some discussion beyond the choices of line graphs vs. pie charts

  15. You learned to read numbers You need to learn to WRITE pictures Or, to be more specific, you need to explain to others what you see in pictures

  16. Question + Data > Picture = Story ?

  17. Bad visualization for one question can be good for another • Who will win the election? • Which regions support the Democrats? Poll dataset did not include Hawaii or Alaska

  18. AN EXAMPLE OF PROGRAM EVALUATION DATA VISUALIZATION BY EXAMPLE

  19. The government is smarter than you think (No, I’m serious)

  20. Was the program implemented as planned?

  21. Was the program implemented as planned?

  22. (This was done in JMP)

  23. Did the program work?

  24. GOPTIONS HBY = 2 ;PROCGPLOT DATA=wussexample UNIFORM; PLOT z_total_post * z_total_pre / VREF=0 ; BY group;

  25. NOTE: Regression equation : z_total_post = 0.13379 + 0.776552*z_total_pre. NOTE: The above message was for the following BY group: group=CONTROL NOTE: Regression equation : z_total_post = 1.233616 + 0.578418*z_total_pre. NOTE: The above message was for the following BY group: group=EXPERIMENTAL Equations in the sas log for the statistician in you

  26. Is the intervention successful under all conditions?

  27. Admittedly, we did not train people while flying on a trapeze Training was administered to four cohorts

  28. Creating the interaction graph First, in the RESULTS window, type sgedit on

  29. Creating the interaction graph First, in the RESULTS window, type sgedit on Ods listing sge = on ; Ods graphics on ; procglm data = plots ; class TestType cohort ; model z_total = TestType cohort TestType*cohort ; where group = "EXPERIMENTAL" ;

  30. Click on the sge plot to edit it

  31. Of course, that is kind of like being the smaller midget Oddly, the most time-consuming part of this is making the lines thicker

  32. Using SGEDIT to, well, edit • Double-click on the .sge file in the RESULTS window • Right-click in the plot area & select PLOT PROPERTIES • Select desired line thickness

  33. Yes, the TestType*Cohort*Group interaction (F=5.84, p < .0001) AND the TestType*Group interaction (F=22.92, p < 0001) in the other repeated measures ANOVA were significant. Thanks for asking!

  34. Looking at the little picture

  35. Graphs sometimes provide better information than numbers (Especially true for small samples)

  36. or… How SAS ODS GRAPHICS can improve your life

  37. Are these test related? R=.22

  38. Look!

  39. Another example • Years of Education as predictor of gain score • R-square = .46 • Correlation = .68) • P <.01.

  40. Now looky here … Is it a real relationship?

  41. What should we do? Throw the score out? Keep the score in? Something else?

  42. Ignoring my partner … Compare your answers with the people next to you

  43. Sometimes outliers are the most interesting part of your study

  44. ODS GRAPHICS ON; <some procedure> ODS GRAPHICS OFF;

  45. PROC CORR

  46. One last example on knowing your data Not just telling a story, having a conversation

  47. PROC FREQ

  48. Custom Map-making How to plot the largest category in a frequency distribution

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