Download presentation

Presentation is loading. Please wait.

Published byKarly Carle Modified over 2 years ago

1
INTRODUCTION TO CLINICAL RESEARCH How To Make A Bad Plot Karen Bandeen-Roche, Ph.D. July 13, 2010

2
How to display data badly Karl W Broman Department of Biostatistics and Medical Informatics University of Wisconsin

3
Using Microsoft Excel to obscure your data and annoy your readers Karl W Broman Department of Biostatistics http://www.biostat.jhsph.edu/~kbroman

4
4 Inspiration This lecture was inspired by H Wainer (1984) How to display data badly. American Statistician 38(2):137-147 Dr. Wainer was the first to elucidate the principles of the bad display of data. The now widespread use of Microsoft Excel has resulted in remarkable advances in the field.

5
5 General principles The aim of good data graphics: Display data accurately and clearly. Some rules for displaying data badly: –Display as little information as possible. –Obscure what you do show (with chart junk). –Use pseudo-3d and color gratuitously. –Label badly –Use a poorly chosen scale. –Ignore sig figs.

6
6 Displaying data well Be accurate and clear. Let the data speak. –Show as much information as possible, taking care not to obscure the message. Science not sales. –Avoid unnecessary frills — esp. gratuitous 3d. In tables, every digit should be meaningful. Don’t drop ending 0’s.

7
7 Displaying data well Show “typical”, “average” values Convey extent of “spread”, “variability” in values Compare groups clearly Label explicitly

8
8 Einarsson K, et al (NEJM 313:277, 1985; reprinted in D-S & T, p. 28 1 st ed) Supersaturation of bile with cholesterol necessary for cholesterol gall stones Female gender and increasing age are risk factors for gall stones Is either gender or age associated with percentage cholesterol saturation of bile? Cross-sectional data on 60 healthy Swedish subjects (31 men, 29 women) who were not obese Supersaturation of Bile Data Set

9
9 Bile Data Set

10
10 “Average” -- typical or representative value; where the distribution is “centered” Different measures of the center -- usually, all the same for symmetric distributions (ones that look on right or left of center Median -- value such that half the observations are less than it and half are greater than it (50th percentile) MalesFemales 86% 84% Mode -- value where the distribution achieves maximum -- most likely value MalesFemales 80-90% (85%)80-90 (85%) Mean -- sum of values divided by the number of values = MalesFemales 84.5%88.5% Measures of the “Average”

11
11 Spread -- variability among the observations Different measures of spread, like averages, represent distinct aspects of distribution Interquartile range –75th-25th percentiles -- range of values that contains middle 50% of data MenWomen 106-66= 40.0%111.5-71=40.5% Measures of Spread

12
12 Variance = (standard deviation) 2 = mean squared error deviation from the mean variance = standard deviation = square root of variance Men Women (24.0%) 2 =574(% 2 )(26.6%) 2 =761(% 2 ) to n from i=1 SUM Measures of Spread (cont’d)

13
13 Displays for continuous data –Histograms / Stem and leaf plots –Boxplots Displays for categorical data: tables Displays for relationships of two variables (on same “people”) to each other –Continuous data: scatterplots –Categorical data: cross-tabulations Some common data displays

14
14 4* 07 5* 2678 6* 567 7* 3489 8* 00667888 9* 0 10* 66 11* 00128 12* 3 13* 7 Stem and Leaf Plot: % Saturation, Men

15
15 Boxplots of Bile Data

16
16 Scatterplot: SBP vs DBP SBP DBP

17
Some really bad plots

18
18 Example 1

19
19 Example 2 Distribution of genotypes AA21% AB48% BB22% missing 9%

20
20 Example 3

21
21 Example 4

22
22 Example 5

23
23 Example 6

24
24 Example 7

25
25 Example 8

26
26 Main points once again Be accurate and clear. Let the data speak. –Show as much data as possible, taking care not to obscure the message. Science not sales. –Avoid unnecessary frills –Go for the cleanest display that conveys the necessary info In tables, every digit should be meaningful. Don’t drop ending 0’s.

27
27 Displaying data well Show “typical”, “average” values Convey extent of “spread”, “variability” in values Compare groups clearly Label explicitly

28
28 Further reading ER Tufte (1983) The visual display of quantitative information. Graphics Press. ER Tufte (1990) Envisioning information. Graphics Press. ER Tufte (1997) Visual explanations. Graphics Press. WS Cleveland (1993) Visualizing data. Hobart Press. WS Cleveland (1994) The elements of graphing data. CRC Press.

Similar presentations

OK

Why do we analyze data? To determine the extent to which the hypothesized relationship does or does not exist. You need to find both the central tendency.

Why do we analyze data? To determine the extent to which the hypothesized relationship does or does not exist. You need to find both the central tendency.

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on hindu religion symbols Ppt on autism standard deviation Ppt on indian army weapons purchase Ppt on natural numbers 0 Ppt on information technology industry in india Ppt on job skills Ppt online open port Ppt on 4-stroke petrol engine Ppt on brain tumor detection Ppt on network theory electrical