What a data point really is, why single numbers mislead, and how sampling, context, and honest charts turn individual observations into real insight.
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What Data Point Does
Explores the data point — the single measurement — and how good observations, honest context, and careful handling turn individual points into insight.
Value, unit, time, and source — the four parts that make a measurement usable.
When a strange point is an error and when it is the most important one you have.
Why which points you collect matters more than how many you collect.
The same number can be great news or terrible news — context decides.
Scatter plots and dot charts that show your data instead of hiding it.
How many observations you need before a pattern deserves your trust.
How Data Point Works
Record value, unit, time, and source so the point still means something later.
Compare against a baseline before drawing any conclusion from one number.
Resist big claims until enough observations agree with each other.
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A single measurement carries no context — no trend, no variation, no baseline. One hot day proves nothing about a summer.
Investigate first. An outlier can be a typo, a sensor glitch, or the discovery of the year. Delete only when you can explain why it is wrong.
There is no magic number — it depends on how noisy the data is. The honest habit is to show the variation, not just the trend line.
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