Loading content
Central tendency summarizes a dataset by identifying a single representative value (mean, median, mode). It guides reporting, comparison and modeling choices by describing the 'center' of distributions. Selection depends on data scale, distribution and outliers; understanding trade-offs is essential for valid interpretation.
Use this profile to understand the building block briefly, place it in the model, and switch to the 360° assessment when needed.
Theoretical construct: explains a term, principle, or mental model.
What you need to understand to reason about a domain.
Central tendency describes which typical or middle value represents a data distribution.
The concept grew from the statistical need to summarize many observations with a meaningful measure. It developed across statistics; NIST documents the methods without naming a single originator.
Place three measuring sticks over the same data: the mean divides the total by the count, the median marks the middle sorted value, and the mode shows the most frequent value. Each reacts differently to outliers and distributions.
It averages all values and is therefore sensitive to extreme observations.
The middle sorted value is more robust to individual outliers.
The most frequent value is useful for discrete or categorical data.
Measures of central tendency support reporting, comparison, and first data analyses. The appropriate measure depends on scale, distribution, and outliers.
Where this building block is located in the topic model.
No structure path available.
Explore how this building block connects to concepts, methods, technologies, and tools.
These sources establish the term and its professional meaning.
All direct connections of the current building block in a compact text view.
This classification shows where the building block typically matters, how demanding it is, and what kind of impact it has in the model.
The level within the organization (enterprise, domain, team) at which the AssetBlock is applied.