Data, 14–17 years — 20 topics · MyLeoNes™ Kuks
20 data topics written for 14–17 years — not a older text simplified. In teaching order, each a deck of five cards: the idea, why it exists, a worked example, the common trap and where you meet it.
Data · 14–17 years
Representative samples
How to learn about a large group by studying a smaller, fairly chosen group.
Mean, median and outliers
Different centres tell different stories about the same list of numbers.
Spread in data
How far apart values are, not just where their centre lies.
Correlation is not causation
A pattern between two measurements does not by itself prove that one causes the other.
Measurement bias
Learn how a measuring method can push results in one direction.
Histograms
See how numerical data are distributed by grouping values into intervals.
Two-way tables and conditional proportions
Compare groups fairly by asking “out of which group?”
Confidence intervals
Describe an estimate together with the uncertainty caused by limited data.
The normal distribution
A model for measurements that gather around a typical value, with fewer results as you move away from it.
Standard scores (z-scores)
A way to say how far a value is from its group’s mean, measured in standard deviations.
Hypothesis tests
A structured way to judge whether data are surprising if a claimed starting explanation were true.
Regression lines and residuals
A line summarises how one numerical variable tends to change with another, while residuals show what the line misses.
Types of data and variables
Before analysing data, identify what each value represents and what kind of values it can take.
Misleading graphs
A graph can display true numbers and still create a false impression through its scale, labels or design.
Fair experiments and random assignment
A well-designed experiment separates the effect of a treatment from other differences between the groups.
Data privacy and anonymisation
Useful data can also reveal people, so collecting and sharing it requires care about identity, purpose and consent.
Percentiles
A percentile tells you how a value compares with the rest of a group. It is useful when position matters more than an average.
Missing data
Blank answers are part of a dataset, not an invisible detail. Understanding why values are missing helps you judge whether a conclusion is trustworthy.
Simpson’s paradox
A comparison can point one way in separate groups and the opposite way when the groups are combined. The change is caused by how many cases each group contains.
Data provenance
Data provenance is the record of where data came from and what happened to it before a conclusion was made. It lets someone check, repeat or correct an analysis.
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