Information Saturation Threshold
Updated 2026-08-10
INTRODUCTION
English translation pending.
CORE DEFINITION
The information saturation threshold marks the point at which collecting more data produces no new categories or insights, because each additional sample's marginal contribution has fallen toward zero. The model tells you when enough is enough: more data is not always better, and continuing to collect past saturation is a pure waste of resources that should already have gone to analysis.
SCAFFOLDING EFFECT
Reduce cognitive load
- Track the new per sample: code as you collect and watch what each new unit actually adds - Call saturation honestly: stop when several samples in a row bring no new category - Budget the minimum: set the sample size from the goal and the resources, not vanity
Anchor fast decisions
In qualitative work the marginal information of each new sample declines as the existing categories fill in, until consecutive samples repeat what is already known and the increment is noise rather than signal. Reaching that point means the current question has been answered sufficiently, so saturation works as an economic stop rule that prevents sampling without end.
MINIMUM ACTION
In progress 0/1Practice this model in one real situation:
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Source support: Explicit
- en.wikipedia.orghttps://en.wikipedia.org/wiki/Information_overloadverified
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