Standard Error
Install and import#
npm install fintech-algorithmsimport { standardError } from "fintech-algorithms/foundations/sampling-estimation-and-statistical-inference/standard-error";Signature#
standardError(input)Reports the standard error of the mean for input.sample — its sample standard deviation divided by the square root of the observation count.
Parameters#
| Name | Type | Notes |
|---|---|---|
input | D00Input | One record whose sample holds the observed data. estimates and alpha are validated for the whole family before dispatch, so they must be present and valid here too.sample: non-empty list of finite numbers, at least two of them · estimates: non-empty list of finite numbers · alpha: strictly between 0 and 1 |
Returns#
D00Output
An object with standardError, sampleSize and sampleStandardDeviation (n-1 denominator).
Errors#
- When sample or estimates is missing, empty, or contains a non-finite number — both are parsed for every topic in the family, whether or not the topic uses them — throws RangeError
- When sample holds fewer than two observations — throws RangeError
- When alpha is not strictly between zero and one — throws RangeError
Complexity: time O(n + m),
space O(n + m).
Worked example#
verified This is the worked example published in the article, replayed by the test suite on every run. The output cannot drift.
Input#
{
"sample": [2, 3, 4, 3, 4],
"estimates": [3, 3.1, 3.2, 3.3, 3.4],
"populationMean": 3.2,
"alpha": 0.05,
"mseBenchmark": 0.1,
"nullMean": 3,
"alternativeMean": 3.5,
"practicalThreshold": 0.1,
"comparisons": 5
}Call#
standardError(input)Returns#
object with 2 fields: standardError, sampleSize
{
"standardError": 0.3741657386773941,
"sampleSize": 5
}Diagrams#
How it works#
This page states the contract — how to call it correctly. The article explains the concept: why it works, and where it breaks.
References#
- Product and Process Comparisons — NIST/SEMATECH e-Handbook
- Confidence Intervals — NIST/SEMATECH e-Handbook
- Historical-example decision