Statistical significance
Statistical significance judges whether an observed effect is unlikely to be due to chance alone.
At a glance
Key signals
0% cross fields · reaches 0 more
- Biostatistics
- Statistics
- Explanation
- Examples
- Misconception
- Sourced relations
- Attribution
Dependencies
What this concept builds on and what it makes possible — derived from the atlas’s dependency, causal and structural relations, not from every related edge.
System context
Statistical significanceis part ofBiostatisticsEstablished
Significance testing is central here.
Mechanism: Deciding whether a medical effect is real rests on the significance tests biostatistics provides.
Structural role & consequence
Interpreted from the current atlas graph — what the connections mean, not just how many there are.
Currently dark in the atlas: no key date stored · 1 of 1 of its relations lack claim-level evidence.
atlas representation · Describes the current Thinking OS representation, not the state of the world.
Structural neighbourhood: 1 → 3 → 23 concepts reachable within 3 hops.
structural · Structural reach — being reachable is not the same as being understood.
All 1 of its relationships stay within its own discipline — a field-specific concept in the current atlas.
structural · Structural graph analysis — not a claim of importance, causation or history.
cross-field
1 within-field, 0 cross-field
Strengths & constraints
Constraints
- Evidence coverage currently thin in the atlas — few of its relationships carry claim-level evidence. atlas representation
- No dated history stored — the atlas records no key date for this concept. atlas representation
Conditions
- Read structurally — most of its relationships carry no external evidence yet, so claims here are graph-derived. structural
Seen through each discipline
How this concept sits in each of its fields — derived from its real connections in the graph, not asserted.
Through this lens it connects to Biostatistics.
Through this lens it connects to Biostatistics.
What it looks like
Concrete cases that make this idea recognisable in the real world.
Significant but trivial
With a million users, an A/B test can show a 'highly significant' 0.01% change in clicks — real, but far too small to matter. Significance answered 'is there any effect?', not 'is it worth acting on?'
Check yourself
A quick check against a common misconception. Nothing is scored — picking the tempting-but-wrong answer just flags an idea worth revisiting.
Which statement is correct?
A statistically significant result is a large, important, and certainly true one.
Significance (a small p-value) only says the data would be unlikely if there were no effect. It says nothing about the effect's SIZE or importance, and with enough data a trivial effect becomes significant. It is not the probability the hypothesis is true.
Related ideas to explore
Concepts that look related but are not yet connected here — candidates for a connection to reason about, not established links.
- Connects 1 other ideas across 2 disciplines.
- Most of its connections are of the “Kind & structure” kind.
Derived from the graph’s real structure — observations, not a score.
Sources
- Introduction to statistical significance verified
- Statistical Significance (2007) verified