# data[].attributes.body
Incorrect statistics (using numbers instead of percentage, or vice versa)

# data[].attributes.headline
Not good: chart/table is too small to see

# data[].attributes.subject
Not good: chart/table is too small to see

# data[].attributes.body
When data is not properly ordered

# data[].attributes.headline
Not good: comparing data that doesn't need to be compared

# data[].attributes.subject
Not good: comparing data that doesn't need to be compared

# data[].attributes.headline
Not good: wrong label/title for chart, lack of description

# data[].attributes.subject
Not good: wrong label/title for chart, lack of description

# data[].attributes.body
Clear legend

# data[].attributes.headline
Not Good: data certified to prove a point

# data[].attributes.subject
Not Good: data certified to prove a point

# data[].attributes.body
Misleading axes

# data[].attributes.headline
Beautiful. But data still needs to be important...worth knowing.

# data[].attributes.subject
Beautiful. But data still needs to be important...worth knowing.

# data[].attributes.body
Scale is not consistent

# data[].attributes.headline
Not good: trying to visualize everything

# data[].attributes.subject
Not good: trying to visualize everything

# data[].attributes.headline
Good: attention to scale - i.e. if data you're representing scales well

# data[].attributes.subject
Good: attention to scale - i.e. if data you're representing scales well

# data[].attributes.body
Colors that are hard to distinguish

# data[].attributes.headline
Good: understandable with minimal explanation

# data[].attributes.subject
Good: understandable with minimal explanation