law of large numbers concept
| Definition | The sample mean settles toward the expectation as the sample grows, its standard error falling with the square root of n, so that halving the error takes four times the data. Primer S, equation S.11. |
|---|---|
| Example | A standard error of 0.707 at n = 8 becomes 0.354 at n = 32. |
| Book | Data Mining as Observation, draft 0.2, commit f3914f0; entry id law-of-large-numbers, kind concept. |
| Status | no ledger row names this entry. Corrections: none recorded. |
| Defining equation | Book equation S.11. |
| Assumptions and scope | none |
| Prior art | none recorded |
| Evidence | none |
| Reviewed | semantic review 2026-09-09; generated 2026-09-10 from records at the commits on the provenance page. |
Equation
Book equation S.11.
\[\operatorname{SE}(\bar x)=\frac{\sigma}{\sqrt n},\qquad \frac{\bar x-\mu}{\sigma/\sqrt n}\ \approx\ \text{normal with mean } 0 \text{ and variance } 1.\]
Conditions
none
Ledger
none
First stated
Primer S section S.4 of Data Mining as Observation, added in draft 0.3 (2026-09-09) for the ECE 514 readers whose first courses are far behind. The idea is standard and TSK Appendix C covers it at length.
Measurements
none
Failures and corrections
none
Invariance envelope
none declared
Machine checked
none
Used in
Data Mining as Observation primer S.
Related
expectation; standard error; central limit theorem; Monte Carlo.
See also
none
Status
Generated 2026-09-10 by encyclopedia/generate.py; book
at observation-data-mining f3914f0; the commit of every record is listed
in the encyclopedia’s provenance.