# data[].attributes.body
One of the bigger things I realized from the last module is how messy real data can be. Empty values in the data, as well as improbable ones, hindered the ability for reasonable conclusions to be reached. However, messy data is what exists in the real world, so students need to be exposed to it as soon as possible.

# data[].attributes.body
An investigation is only as good as the investigator's skills in using the statistical technology resources available to them.

# data[].attributes.body
A statistical investigation should have purpose and utility!

# data[].attributes.headline
One of the things that I generally disagree with is _not_ having an organizing question that presents a useful or actionable answer 

# data[].attributes.subject
One of the things that I generally disagree with is _not_ having an organizing question that presents a useful or actionable answer 

# data[].attributes.headline
working with real data can be messy,  with values missing or invalid.   Clean data can have a big impact on the size of final data set.

# data[].attributes.subject
working with real data can be messy,  with values missing or invalid.   Clean data can have a big impact on the size of final data set.

# data[].attributes.body
real data is meaningful to students; moreso than "random" numbers that fit a particular topic

# data[].attributes.body
Cleaning the data is a critical step!

# data[].attributes.body
cleaning data is a task in and of itself

# data[].attributes.body
-students don't typically go through the full investigative cycle in class - but they should in order to really understand the utility of statistics
-investigations should use real data
-students should be part of the posing questions

# data[].attributes.headline
The importance of having data with at least some "mess" in the frame to facilitate critical review of the data, cleanup, and scanning for contradictions and correlations

# data[].attributes.subject
The importance of having data with at least some "mess" in the frame to facilitate critical review of the data, cleanup, and scanning for contradictions and correlations

# data[].attributes.body
Using different variables
Using different attributes

# data[].attributes.body
Thinking about the task/project from start to finish, developing a clear research question, to survey design, to variable type, to the actual collection of the data, cleaning of the data,  and then finally to analysis of the data (and how different people can see the problem differently).  Context is Key!! & Being able to be a holistic thinker will serve you and your students well in statistics!!

# data[].attributes.body
Use  real data (that might need clean up).
Pose questions of interest (not restricted to the classroom).
Look beyond the data.

# data[].attributes.body
A statistical investigation's meaning can be deepened if students know that their investigation will create something or be beneficial to others.

# data[].attributes.body
Cleaning the data is a critical step.