# data[].attributes.body
The middle line shows where half the data lies?

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The dots represent each students count of instagram followers. The lines represent the lower, upper, quartiles and median

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The dots represent each students count of instagram followers. The lines represent the lower, upper, quartiles and median

# data[].attributes.body
The numbers are how many Instagram Followers.

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The x-axis is the no. of instagram followers, the lines indicate the range of the data, from min to max.

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Each dot represents the number of followers a student in our data has

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horizontal lines from each end of box seem to indicate lower and upper values

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horizontal lines from each end of box seem to indicate lower and upper values

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The median number of followers is clearly higher for females than for males.

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female tends to have more instagram followers than males

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The lines show the min and max, whole of the box shows the IQR

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The lines show the min and max, whole of the box shows the IQR

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comparison of instagram followers for male and females

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Where does this data come from?

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Where did the data come from? This helps students understand the data source and perhaps a hidden agenda 

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Where did the data come from? This helps students understand the data source and perhaps a hidden agenda 

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what is the population involved in this investigation

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what is the population involved in this investigation

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for what kind of age group?

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What does this data show - might facilitate some contextual consideration when thinking of data and stats

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What does this data show - might facilitate some contextual consideration when thinking of data and stats

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Why are we doing this and where in our real life do we use this?

Teachers can relate the problems to real-life concepts and show them the usage of statistical representations in their real life so that they can understand the contextual importance of statistical problems. This will enhance engagement.

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Does this pattern apply to the whole population or just this sample?

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Worry questions can help us come to a reasoned judgment and can help mitigate our personal biases to some extend

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Worry questions can help us come to a reasoned judgment and can help mitigate our personal biases to some extend

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worry questions are questions that need to be activated when conducting an investigation or forming an opinion on a media report or graph. It helps students to pose their investigation question, write their plan for their investigation, identify their variables.

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visual representation

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Practice! Low stakes, low pressure.

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i think that teachers modelling the language is very important, especially for esol students

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Teacher modelling with illustrations

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Use those 'stats words' in the class, before introduce them formally?

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Use those 'stats words' in the class, before introduce them formally?

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using visual examples, such as ask the kids to lie up from tallest to shortest, to see the distribution of their height

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Seeing examples of strong descriptions and receiving targeted feedback helps students improve.

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Personally, using data examples and contexts that are more familiar like we just discussed(e.g instagram followers) make it easier to follow and understand for the students.

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Personally, using data examples and contexts that are more familiar like we just discussed(e.g instagram followers) make it easier to follow and understand for the students.

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Define the Problem by begin with a real, meaningful question and clarify what is being investigated and students must understand why the data was collected.

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graphs, to guide students go through it

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Appropriate use of statistical words, especially those with different meanings in daily life (e.g., random, significant, ...)

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Questions 100% - The more they question the more they can reflect on how they are thinking and how they might adjust their perspectives based on the answer they might receive from us (or from sharing)

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Questions 100% - The more they question the more they can reflect on how they are thinking and how they might adjust their perspectives based on the answer they might receive from us (or from sharing)

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examples, especially example with graphs

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examples, especially example with graphs

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Again, feedback and revision

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make sure students know the statistical langauge, and listen to use them say it in classroom throughout teaching

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understanding the context and Practice using precise terminology like “distribution,” “outliers,” “association,” and “trend.”

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Actually focusing on this in class rather then letting students muddle through; building on what they know and having them engaged in the making sense process

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The direction of the skewness is the more spread-out tail

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I used to explain it as a slide though that might not be as insightful as your example 

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I used to explain it as a slide though that might not be as insightful as your example 

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high spread - far apart

low spread - close together

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Imagine the dot plot is on a seesaw. The mean is where the seesaw balances.

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Questioning and repeating back their words and linking to the technical jargon with examples

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Questioning and repeating back their words and linking to the technical jargon with examples

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go back to the data and try find out why the 'skewness' happen

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go back to the data and try find out why the 'skewness' happen