From Earlham CS Department
Jump to navigation
Jump to search
|
|
| Line 1: |
Line 1: |
| − | # Have an idea or causal theory that directs the gathering and assessment of evidence. Follow these tips:
| + | |
| − | *put data in an appropriate context for assessing cause and effect
| |
| − | *make good quantitative comparisons including examining why something did not occur
| |
| − | *consider alternate explanations
| |
| − | *assess possible errors
| |
| − | ['''Visual Explanations''', page 29-36. emp]
| |
| − | # Multiples are useful when analyzing multi-variable processes. Allows for the combination of detail and overview on one display. Combine multiples with narrative text and images for monitoring data-rich processes. ['''Visual Explanations''', page 110. emp]
| |
| − | # Graphic displays should have simplicity of design and complexity of data. This makes comprehension easier, while still maintaining a large amount of data. Avoid unnecessary symbols and decorations, while showing detail. [''' The Visual Display of Quantitative Information''', page 177. emp]
| |
| − | # Avoid “cherry-picking” by presenting data in a way that is not skewed for a specific benefit. Ask questions such as, “Do the findings grow from the evidence or from the process of evidence construction?”, “Would the process survive a research audit?”, and “Are the findings too good to be true?” [''' Beautiful Evidence''', page 144-147. emp]
| |
Latest revision as of 07:24, 27 August 2012
