See, this group is way bigger than that one, the bar is twice as tall.
Student starts the vertical axis at a value well above zero, so a tiny difference between bars looks like a giant one, and calls the plot a fair picture of the data.
They focus on making the bars look different and forget that the axis scale changes how big the gap appears. They treat the picture, not the numbers, as the truth.
This one responds to correction in the moment — the idea is there, the rule being applied is not.
Leaves out part of the data
What you might hear
I just left off that one weird high value, it would have made the chart look messy.
Student drops some data values when building the plot, often the extremes or an inconvenient group, and treats the plot as if it shows the whole data set.
They think the plot is just a rough sketch, so a few missing points feel harmless. They do not see that the plot is supposed to stand in for all the data.
This one responds to correction in the moment — the idea is there, the rule being applied is not.
Reads a summary plot as every value
What you might hear
This bar is at 5, so somebody got a 5 on the test.
Student looks at a box plot or histogram and talks about individual data points, like saying a bar of height 5 means one student scored 5, instead of reading it as a summary of grouped data.
They learned dot plots, where each dot is one value, and carry that over. They do not notice that a box plot shows quarters and a histogram shows counts in bins, not single values.
This one responds to correction in the moment — the idea is there, the rule being applied is not.
Computes the median without ordering the data first
What you might hear
The median is the one right in the middle. I just counted to the middle of the list.
For the five-number summary, the student picks the middle value of the data as it is listed instead of sorting the numbers from low to high first. This gives a wrong median, and wrong quartiles too.
They learned median as the middle number and grab whatever sits in the middle of the list, forgetting that median only means middle after the data is in order.
This one responds to correction in the moment — the idea is there, the rule being applied is not.
Draws the box from the minimum to the maximum
What you might hear
The box covers everything, so it goes from the smallest number to the biggest number.
When building a box plot, the student stretches the box from the lowest value to the highest value, instead of from Q1 to Q3. The whiskers then have nothing left to reach.
They think the box should hold all the data, so they anchor it on the smallest and largest numbers rather than on the first and third quartiles.
Words rarely shift this one. It needs something to look at — a picture, a fold, an object on the table — before the explanation lands.
Makes histogram bins of unequal width or leaves gaps
What you might hear
I left a little gap between each bar like a bar graph, and I made some bins wider.
When drawing a histogram, the student uses bins of different widths or leaves spaces between the bars. A histogram needs equal-width bins with bars that touch.
They treat a histogram like a bar graph of separate categories, so they space the bars apart and size the intervals by feel instead of by a fixed width.
Words rarely shift this one. It needs something to look at — a picture, a fold, an object on the table — before the explanation lands.
What to say when you see it
For each mistake above, Algo School gives you the words to say next — the specific response that corrects the thinking instead of just marking the answer wrong, plus practice aimed at that exact error.
The box plot summarizes the data set 0, 2, 3, 6, 9, 11, 15. What is the maximum of this data set?
A15
B6
C2
D0
AnswerA — 15
Generated by univariate-stats-alg1, a deterministic question engine — not written by an AI, and verified before any child sees it.
FAQ
Questions parents ask about this
How do I know which of these mistakes my child is making?
Look at what they SAY, not just what they wrote. Each mistake above includes the phrasing a parent typically hears — that sentence is usually the giveaway. In Algo School, the tutor recognises these patterns while your child is working and adapts the session around the one it sees.
Is "Represent data with plots" something children normally struggle with?
Yes. We track 6 distinct approved mistake patterns for this concept, each one written up because it recurs — not because it is unusual. A child making one of them is doing something systematic, which is far easier to fix than random errors.
What should I do once I know which mistake it is?
Respond to the thinking, not the answer. Algo School gives parents the specific wording for each mistake, then generates practice targeted at that error and shows you whether it actually cleared.
Where do these mistakes come from?
They are part of the same content library that drives practice: each one is reviewed and approved before it is used, and the Interpreting Categorical and Quantitative Data questions on this site are checked by an automated verification pass before a child is ever served them.
Related
Other places children go wrong
Where this sits
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