1. Writing The rest of the paper…

2. Methods Emulate Err on the side of too much detail Why not start today?

3. Results Results are different from data! Data = the numbers themselves = most belong in figures and tables Results = the meaning (or analysis) of the data = the text of the Results section

4. Results pertinent to the main question(s) asked Summarize the data; report trends and statistics Cite figures or tables that present data Results

5. Overall, the mortality rate of organisms experiencing experimental stressors was approximately six times higher than for organisms under control conditions (Fig. 1). This effect is significant (i.e. the 95% CI does not overlap zero; Fig. 1). There was significant heterogeneity in overall mortality among experiments (QT = 951.57, df = 335, P < 0.0001). The total suicide rate for Australian men and women did not change between 1991 and 2000 because marked decreases in older men and women were offset by increases in younger adults, especially younger men (Fig. 3).

6. Results: tips Use subheadings – relate to questions Include negative and control results Show magnitude of response/ effect (e.g., as percentage) Reserve the term “significant” for statistically significant Do not discuss rationale for statistical analyses (?)

7. Results: tense Use past tense, except to talk about how data are presented in the paper e.g.: Women were more likely to… Frog numbers declined in the South… but: Figure 1 shows… Table 1 displays… The data suggest Remember to use the active voice as much as possible

8. Information was available for 7766 current cigarette smokers. Of these, 1216 (16%) were classified as hardcore smokers. Table 1 gives characteristics of all the smokers. The most striking difference was that hardcore smokers were about 10 years older on average and tended to be more dependent on tobacco. Significantly more hardcore smokers had manual occupations, lived in rented accommodation, and had completed their full time education by the age of 16 years. There was no difference by sex. Results Jarvis et al. 2003. Prevalence of hardcore smoking in England, and associated attitudes and beliefs: cross sectional study BMJ 326:1061

9. Results: reporting stats Goal: Comparison of numbers of birds in natural and exotic forests Stats: t = 2.51, df = 39, P = 0.016 Bad: The observed t value (2.51) was greater than the critical t value (2.02) for 39 degrees of freedom, indicating that we can reject the null hypothesis of no difference between the treatments with a confidence level of 95%.

10. Results: reporting stats Acceptable (only just) There was a significant difference in the number of birds between natural and exotic forests (t = 2.51, df = 39, p = 0.016). Best There were significantly more birds in natural than in exotic forests (t = 2.51, df = 39, p = 0.016).

11. Figures and tables General rules One table or figure per page – at end of manuscript Table or figure + its caption make a stand-alone story Data presented in one format only Do not present raw data (?) Number consecutively in the order in which they appear in the text Separate numbering for figures and tables

12. Tables Caption Goes at top of table Identifies briefly the specific topic or point of the table Uses the same key terms as in the column headings and the text of the paper Table 2. Effects of QTL genotype on placental and embryonic gene expression and weight, birth weight and litter size. mRNA and protein levels are expressed relative to reference samples run in each assay. Values are least squares means ± standard error.

13. Tables Caption Goes at top of table Identifies briefly the specific topic or point of the table Uses the same key terms as in the column headings and the text of the paper Format Follow journal guidelines Only a few horizontal lines May use short horizontal line to group subheadings No vertical lines!

14. Tables: example Stoving. 1999 J Clin Endocrinol Metab 84: 2056-2063 Caption at top Same terms in headings and caption 3 lines only

15. Figures Caption Goes at foot of figure Identifies briefly experimental details Gives definition of symbols, shading, and stats Figure 1. Survival of Galleria mellonella larvae infected with strains of Aspergillus fumigatus. (A) Mean number of larvae alive at each day after injection with each clinical strain. (B) Mean number of larvae alive at each day after injection with each environmental strain. Means are of six replicates for all of the strains, except UAMH 3762, for which 4 replicates were performed. Mating type is indicated by line type: solid lines for MAT1-1 strains and dashed lines for MAT1-2 strains. Strain names are included next to each line. (C) Means of all strains within a group. Circles indicate MAT1-1 strains, triangles indicate MAT1-2 strains, filled symbols indicate strains of clinical origin, open symbols indicate strains of environmental origin, and error bars indicate standard errors. Because of the large number of strains, data are not presented as Kaplan–Meier curves to improve clarity.

16. Figures Caption Goes at foot of figure Identifies briefly experimental details Gives definition of symbols, shading, and stats Format Follow journal guidelines Variable Primary evidence, e.g. electron micrographs, gels, etc. Graphs, e.g. scatter, bar, boxplots, etc. Diagrams or drawings, e.g. model, experimental set-up, etc.

17. Figures: Primary evidence Figure 1. Transcription of antisense RNA leading to gene silencing and methylation as a novel cause of human genetic disease Cristina Tufarelli et al. Nature Genetics (2003)

18. Figures: Primary evidence Zucca et al. 1998. NEJM 338: 804 Figure 1. Histologic patterns in the evolution from chronic gastritis to gastric lymphoma.

19. Figures: Graphs Figure 3. Hypertension Prevalences in 6 European and 2 North American Countries, Men and Women Combined, by Age Group JAMA Vol. 289 No. 18, May 14, 2003 Figure 3. Hypertension prevalences in six European and two North American countries for men and women combined, by age group. Line graphs Used to show changes (e.g. over time)

20. Figure 1. The relationship between the percentage of body fat and the serum leptin concentration in 136 normal-weight and 139 obese subjects. Considine et al. 1996. NEJM 334: 292 Figures: Graphs Scatter plots Used to show relationships between two variables

21. Figures: graphs Figure 2- Relationship between BMC of the forearm/heel and time since menarche. *Significantly different than forearm BMC of group 1 (< 1 yr since menarche); BMCA: forearm BMC; BMCH: heel BMC. Medicine & Science in Sports & Exercise 2003; 35(5):720-729 Bar graphs Used to compare groups

22. Figures: Diagrams Talan et al. 1999. NEJM 340: 85 Figure 1. Location of wound infections in 50 patients bitten by dogs and 57 patients bitten by cats.

23. Figures: Diagrams Figure 2: Proposed pathways among disordered eating, menstrual irregularity, and low BMD. Solid lines represent associations suggested by the current study; dashed lines represent associations suggested by previous studies.

24. Figures: Diagrams Fig. 1. Location of the study beaches around the island of Barbados. Fish et al. 2008 Ocean and Coastal Management

25. What about pie diagrams? Pie charts are for children and politicians!

26. Statistics and power analysis Traditional statistics Null hypothesis (H0): –no difference/ relationship Alternative hypothesis: –there IS a difference/ relationship Can the null hypothesis be rejected? Bayesian statistics

27. Statistics and power analysis Traditional statistics Reality: Null hypothesis is trueNull hypothesis is false What you do: Accept null hypothesis Reject null hypothesis Error! α=0.05 P-value (sort of) Error! β=??? 1-β = statistical power If the null hypothesis is false, what is the probability that you will reject it?

28. Statistics and power analysis Traditional statistics Reality: Null hypothesis is trueNull hypothesis is false What you do: Accept null hypothesis Reject null hypothesis Error!  =0.05 P-value (sort of) Error! β=??? 1-β = statistical power If there really is an effect, what is the probability that you will detect it?

29. What affects power? Sample size Variability Effect size  -level

30. Sampling distributions and power Mean of sample Frequency  = 0.05 Null hypothesis: μ = 0

31. Sampling distributions and power Frequency  = 0.05 Null hypothesis: μ = 0 Mean of sample Frequency 1 – β = power Reality: μ = 2

32. Effect of sample size Frequency  = 0.05 Null hypothesis: μ = 0 Mean of sample Frequency Reality: μ = 2

33. Effect of variability Frequency  = 0.05 Null hypothesis: μ = 0 Mean of sample Frequency Reality: μ = 2

34. Original example Frequency  = 0.05 Null hypothesis: μ = 0 Mean of sample Frequency Reality: μ = 2

35. Effect size Frequency  = 0.05 Null hypothesis: μ = 0 Mean of sample Frequency Reality: μ = 4

36.  -level Frequency  = 0.10 Null hypothesis: μ = 0 Mean of sample Frequency Reality: μ = 4

37. When should you do power analysis? Before your experiment After your experiment

38. Power analysis after your experiment E.g., with sample size X, you observed effect size Y, and variability Z Silly power analysis: –What is the power to detect a difference of Y with sample size X and variability Z? Useful power analysis: –What is the power to detect a biologically important effect with sample size X and variability Z?

39. How to present “negative” results Power analysis Confidence intervals –The confidence interval for the difference between group A and group B is … –The confidence interval for the strength of the relationship between A and B is …

40. Discussion Not a detective story

41. Introduction structure BIG picture General background, i.e., what is known The gap: what is not known Your question/ goals Your approach

42. Discussion structure Your results Address your question Put results in context: How does it fill a gap? BIG picture

43. Things you can do (to improve your writing) Read, pay attention, and imitate. Let go of “academic” writing habits Talk about your research before trying to write about it. Search for the right word rather than settling for any old word. Try not to bore your audience. Stop waiting for “inspiration.” Accept that writing is hard for everyone. Revise. Learn how to cut ruthlessly. Never become attached. Find a good editor!