1. EHS 655 Lecture 22: Technical writing, data presentation

2. What we’ll discuss today
Technical writing
Presenting results clearly
Examples of good and bad graphics
Examples of good and bad tables

3. TECHNICAL WRITING
Altman, 1980

4. Technical writing
Use active voice wherever possible (more interesting, less wordy)
Passive voice: “Treatment guidelines for Merkel cell carcinoma were reported by Bichakjian.”
Active voice: “Bichakjian reported treatment guidelines for Merkel cell carcinoma.”
Use subheadings to organize text
Johnson, 2008

5. Technical writing
Clear and simple messages are not the same as “dumbing it down”

6. Technical writing approaches
Traditional and most-universally accepted
Executive Summary
Objectives
Background
Methods
Results
Conclusions
Ehrenberg, 1982

7. Potential issues to address in report
Nieuwenhuijsen, 1997

8. PRESENTING RESULTS CLEARLY
Data visualization is incredibly powerful
Can also be incredibly misleading
UN, 2009

9. Presenting results clearly
UN, 2009

10. Presenting results clearly
Keep formatting consistent throughout text and across tables
Same fonts
Same heading style
Same borders

11. Presenting results clearly
UN, 2009

12. Presenting results clearly

13. Presenting results clearly
Bad – y-axis doesn’t start at zero
Good – y-axis starts at zero

14. Examples of good and bad graphics
Bad –suggests relationship between categories
Good
faculty.up.edu/lulay/mestudentpage/graphexamples-how-to-do.pdf

15. Examples of bad graphics

16. Examples of good and bad tables
UN, 2009

17. Examples of good and bad tables

18. Example of bad exposure table

19. Example of good exposure table
Heederik, Bolei, Kromhout, Smid, 1991

20. Presenting results clearly
UN, 2009

21. Presenting data efficiently – sort!
; UN, 2009

22. Presenting results honestly: scale
Critical - avoid temptation to manipulate
Use scales analyses conducted in
Altman, 1980

23. Presenting variability in results
Standard deviation (SD) vs standard error (SE)
SD = variability of raw data around mean; mean ± SD often reported
SE = precision of mean estimate
SE is always (much) smaller
SD should not be used when data not normally distributed
Consider median, 10th and 90th percentiles

24. Presenting numerical precision
Rarely necessary to present results beyond 3 significant figures
Implies precision we typically do not have
Reducing precision in presented data often makes trends more apparent
0.034  0.03
0.045  0.05
0.067  0.07

25. Presenting data in 3D (Hint: don’t!)

26. Presenting data in 3D (Hint: don’t)

27. Presenting data clearly (not)

28. Presenting data clearly (not)

29. Presenting data clearly (not)

30. Presenting data clearly (not)

31. Presenting data clearly (not)
Make sure numbers add to 100% where appropriate

32. Presenting data clearly (not)
Use consistent bin sizes
Notice anything unusual post-2005?

33. Presenting data clearly (not)
Don’t waste ink

34. Presenting data clearly (not)
Legibility matters!

35. Presenting data clearly
UN, 2009

36. Presenting data clearly
Pie charts CAN be useful

37. Resources Data display UN Making Data Meaningful
UN Making Data Meaningful
The top 10 worst graphs

38. Appendix Writing examples Suggested rules for data presentation via
Bar charts
Line charts
Scatterplots
Boxplots

39. Writing – example 1 (bad)
Two sentences, 100 words, 15 words 3 syllables or more
Ehrenberg, 1982

40. Writing – example 2 (better)
70 words, two sentences, 2 words with 3 syllables
Ehrenberg, 1982

41. Writing – example 3 (good)
40 words, two sentences, 2 words with 3 syllables
Ehrenberg, 1982

42. Suggested rules for pie charts

43. Example of bad pie chart

44. Suggested rules for bar charts

45. Example of bad bar chart

46. Suggested rules for line charts

47. Example of bad line chart
UN, 2009

48. Suggested rules for scatterplots

49. Example of bad scatterplot
Math241/StatTopics/ScatGen.htm

50. Suggested rules for boxplots

51. Example of bad box plot