Presentation on theme: "Hints for better data display Why data display? Good ideas for future presentations Data display is never perfect Judges."— Presentation transcript:
Hints for better data display Why data display? Good ideas for future presentations Data display is never perfect Judges
My approach today I will show good and bad examples As I proceed ask yourself how each figure could be improved. I will also list some rules that you could use as guidelines.
Goal of display? Strive to promote evaluation of objectives/hypotheses Clearly depict relationships Figures are usually preferable to tables Use tables if the exact values are important Very simple comparisons don’t require a figure or table
Flight speed of quail encumbered and unencumbered by radio transmitters Without transmitter 22 MPHWith transmitter 17 MPH Without transmitter 21 MPHWith transmitter 17 MPH Without transmitter 17 MPHWith transmitter 20 MPH Without transmitter 15 MPHWith transmitter 17 MPH Without transmitter 19 MPHWith transmitter 11 MPH Without transmitter 18 MPHWith transmitter 12 MPH Without transmitter 16 MPHWith transmitter 11 MPH Without transmitter 23 MPHWith transmitter 18 MPH Without transmitter 25 MPHWith transmitter 18 MPH Without transmitter 14 MPHWith transmitter 17 MPH Without transmitter 11 MPHWith transmitter 12 MPH Without transmitter 21 MPHWith transmitter 15 MPH Unencumbered Flight speedEncumbered Flight speed
Table 1. What was wrong? 1. Conclusion difficult to see 2. Too messy 3. Units? 4. Font too small in some places
Flight speed of 11 quail encumbered and unencumbered by radio transmitters 22 17 21 17 15 17 19 11 18 12 16 11 23 18 25 18 14 17 11 12 21 15 Unencumbered Speed KPHEncumbered speed KPH
Same data in a figure and a table Mean Flight Speed of Quail With and Without Transmitters. With Transmitters Without Transmitters 15.4 KPH18.5 KPH W Trans W/O Trans Mean Flight Speed of Quail With and Without Transmitters
Which was Better? In this case I wouldn’t use a figure or a table. Average flight speed of unencombered quail was 3.1 Km/hr faster than Quail encumbered by transmitters. The difference is statistically significant (t=2.24, P=0.02 DF=10).
More Complex Data Proportion of arthropods in the diets of male and female North American and European Kestrels
Patterns are more easily interpreted when data are displayed in figures But: Make sure the figure is easy to interpret The best figures are able to stand alone, i.e., they don’t need any help from the presenter So, for every figure ask yourself: “Is there a better way to display the data”?
Kestrel data poorly displayed; interaction difficult to see % Insects in Diet Winter Spring
Stacked bars are almost never your best choice Winter Spring % Insects in Diet
The interaction shows up easily here, could it be better? Winter Spring % Insects in Diet
For bar graphs Put bars together to illustrate the conclusions you are trying to display. Make sure that your eye doesn’t have to travel back and forth across the figure in order to draw a conclusion
For example Given 4 breast cancer cell lines and 4 chemotherapies If you are interested in how the different cell lines respond to the treatments:
Group the data for each cell line together % Apoptosis
However, If you are interested in the efficacy of the different treatments among cell lines:
Group the data for the different treatments % Apoptosis
Rule 3: Display Error Bars If you have replication of continuously distributed data you should display error bars. Standard error, 95% confidence intervals and standard deviation are commonly used. Don’t chose arbitrarily, know your error bars and what they mean
Summary of common mistakes in data display Too many bars or lines are included Lines connect independent data points Raw data are displayed Error bars are absent
More common mistakes in data display Tables are used when a figure is more appropriate Figures don’t illustrate anything Figures are too complicated The “wrong” figure type is used
Statistics and Study Design Most of the time fairly simple statistical tests will suffice: For categorical comparisons and proportions: Chi-squared For two continuously distributed variables in a linear relationship: linear regression For comparison of two means: t-test For comparison of three means: ANOVA
Be careful All of the above require independent and randomly drawn samples (one exception) What if you take a before and after measurement on a set of subjects? What if you take multiple measurements on each member of a sample population?