# data[].attributes.body
Hint: use terms like sector angle, percentage

# data[].attributes.body
Hint: use terms like sector angle, percentage

# data[].attributes.body
Hint: use terms like sector angle, percentage

# data[].attributes.body
Hint: use terms like sector angle, percentage

# data[].attributes.body
Hint: use terms like sector angle, percentage

# data[].attributes.body
For EACH QUESTION:

In your group, discuss to surface what is/ are the feature(s) in the graph that is/ are misleading.

Use the “scaffolds” to answer the question.

Each person will put up one post.

It could be different from your member’s as there maybe more than one misleading feature.

[1 Point for each correct part]

# data[].attributes.body
Hint: use terms like sector angle, percentage

# data[].attributes.custom_properties.8jUJDM-4
The title makes a conclusion for the reader ('huge improvement in attendance') rather than allowing them to interpret the data.

# data[].attributes.custom_properties.RGCxReRY
It influences the reader to believe the increase is significant, even if the actual value shows otherwise.

# data[].attributes.custom_properties.UMO9Srr0
The title may lead reders to assume a dreastic drop though the grapt lacks clear scale of numbers to support that.

# data[].attributes.custom_properties.8jUJDM-4
People may not notice the year at the bottom X axis and think that the change is greater than it actually is.

# data[].attributes.custom_properties.RGCxReRY
The intervals in the year are not equal. 

# data[].attributes.custom_properties.UMO9Srr0
It jumps from 2004 to 2008 and also jumps from 2008 to 2010

# data[].attributes.custom_properties.8jUJDM-4
It doesnt tell the reader what the starting value is

# data[].attributes.custom_properties.UMO9Srr0
Without the starting value, the reader would not be able to know how to read the graph info

# data[].attributes.body
q3

# data[].attributes.custom_properties.8jUJDM-4
it doesn't tell the reader what the starting value is

# data[].attributes.custom_properties.UMO9Srr0
without it the reader wouldn't know how to read it

# data[].attributes.body
^ A man ^

# data[].attributes.custom_properties.8jUJDM-4
The conclusion was written without the reader needing to read the question.

# data[].attributes.custom_properties.RGCxReRY
The title already has a conclusion ("HUGE Improvement in attendance")

# data[].attributes.custom_properties.UMO9Srr0
This will trick the reader into thinking there is an enormous difference in attendance, whereas there might not be that big of a difference.

# data[].attributes.custom_properties.8jUJDM-4
Each parts of the pie charts are separated so it is hard to compare.

# data[].attributes.custom_properties.UMO9Srr0
The 50% part does not cover 50% of the chart.

# data[].attributes.body
the misinterpretation is that the gaps between the years changes rnadomly

# data[].attributes.custom_properties.8jUJDM-4
it causes people to think there is a huge difference in two blocks 2004 and 2008

# data[].attributes.custom_properties.8jUJDM-4
 title makes a conclusion for the reader (huge improvement in attendance) which leads to misinterpretation

# data[].attributes.body
The heading says "Huge Improvement in Attendance"

# data[].attributes.custom_properties.8jUJDM-4
its misleading because the heading makes a conclusion for the reader, rather than allowing the reader to interpret the data. It influences the reader to believe the increase is significant, even if the actual values show otherwise. The title may lead readers to assume a drastic improvement, though the graph lacks clear scale or numbers to support that.

# data[].attributes.custom_properties.RGCxReRY
The heading says "Huge Improvement in Attendance"

# data[].attributes.custom_properties.8jUJDM-4
readers may think that the years are all consecutive and not realise the year jumps

# data[].attributes.custom_properties.RGCxReRY
the years are not consistent, the first 3 are consecutive but the next 2 bars are a big jump

# data[].attributes.custom_properties.UMO9Srr0
there is a 4 and 2 year gap between the third and fourth bar, and the fourth and fith bar which might confuse readers

# data[].attributes.body
erm yes

# data[].attributes.custom_properties.8jUJDM-4
people may think that there was a huge amount of attendance 

# data[].attributes.custom_properties.RGCxReRY
the height of the bars are very exaggerated

# data[].attributes.custom_properties.UMO9Srr0
2010 has not more than 100 students than 2002 but the height of the bar is like way more than 2002's

# data[].attributes.body
First reason

# data[].attributes.custom_properties.8jUJDM-4
The intervals between the bars are uneven, some years are missed out.

# data[].attributes.custom_properties.RGCxReRY
Readers will have a hard time finding the number of attendees in the missing years. Without the years, there will be inaccurate information and assumptions.

# data[].attributes.custom_properties.UMO9Srr0
Although there is an increase in attendance, there is no attendance for the missing years, which could mean that the school closed down, but we would never know.

# data[].attributes.custom_properties.8jUJDM-4
It influences the reader to believe the decrease is significant, even if the actual values show otherwise. 

# data[].attributes.custom_properties.RGCxReRY
The title makes a conclusion for the reader, rather than allowing them to interpret the data.

# data[].attributes.custom_properties.UMO9Srr0
The title may lead readers to assume a drastic drop, but the graph lacks actual numbers and scale to support that.

# data[].attributes.body
There is no scale on the graph

# data[].attributes.custom_properties.8jUJDM-4
It is misleading as there is no scale and the reader might assume that the starting value is 0, and make assumptions by the size of the bars.

# data[].attributes.custom_properties.UMO9Srr0
The bar for year 2004 seems like 2/3 of the bar for year 2010, even when it may not be.

# data[].attributes.custom_properties.8jUJDM-4
The intervals between each bar is equal, but the difference of the years is unequal.

# data[].attributes.custom_properties.RGCxReRY
Readers may have a confusing time reading this bar, wondering where all the other pieces of data are.

# data[].attributes.custom_properties.UMO9Srr0
The school may have a terrorist attack and got burned down by a monkey in the toilet during years that were never mentioned.

# data[].attributes.body
question 4

# data[].attributes.custom_properties.8jUJDM-4
10% and 7% looks like it is quarter of the pie chart and quarter of the pie chart normally is supposed to be 25%

# data[].attributes.custom_properties.UMO9Srr0
the pie chart is represented wrong and will confuse everyone.

# data[].attributes.custom_properties.8jUJDM-4
The reader would think that flights plus cinemas and groceries is equal to 50%.

# data[].attributes.custom_properties.RGCxReRY
Flights are 50% but is not taking up 50% of pie chart.

# data[].attributes.custom_properties.UMO9Srr0
The reader would then be confused when the total of flights plus cinemas plus groceries is not 50% and will wrongly assume that flights are 42%.

# data[].attributes.custom_properties.8jUJDM-4
It makes the reader believe the decrease its significant when the actual values show otherwise. 

# data[].attributes.custom_properties.RGCxReRY
The title makes a conclusion for the reader rather than allowing them to interpret the data.

# data[].attributes.custom_properties.UMO9Srr0
the school might have been wanhoutised but were never mentioned