Module 8 · Sampling, Estimation, and Statistical Inference Lesson 76 of 120
Standard Error
Measuring uncertainty in a mean rather than variation among payments.
Transcript
19 sentences · select one to jump thereCheck your understanding
Which is the larger number here: sample SD or mean SE?
Code lab
Run it yourself
The lesson source in 7 languages. Edit it, run TypeScript and Python right here, and compare with the expected output.
/**
* Fintech Math Bootcamp · Lesson 076 of 120
* Standard Error
* Module 08: Sampling, Estimation, and Statistical Inference
*
* Scenario: Measuring uncertainty in a mean rather than variation among payments
* Rule: estimated SE(mean) = sample SD / √n
*
* Try it: Which is the larger number here: sample SD or mean SE?
*
* Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/sampling-estimation-and-statistical-inference/standard-error/
* Free course: https://courses.thefintechbuilder.com
* Synthetic teaching example, not financial advice or a production library.
*/
export function lesson076() {
const x=[2,3,3,4,4];
const mean=x.reduce((s,v)=>s+v,0)/x.length;
const variance=x.reduce((s,v)=>s+(v-mean)**2,0)/(x.length-1);
const result={mean,sd:Math.sqrt(variance),se:Math.sqrt(variance/x.length)};
return result;
}
export const checkedResult = {"mean":3.2,"sd":0.8366600265340756,"se":0.3741657386773941};
// Run this file directly: npx tsx lessons/08-sampling-estimation-and-statistical-inference/076-standard-error.ts
if (process.argv[1] && import.meta.url.endsWith(process.argv[1].replace(/\\/g, "/").split("/").pop()!)) {
console.log(JSON.stringify(lesson076(), null, 2));
}
Your output
Press Run to execute the code in your browser.
Expected output
{
"mean": 3.2,
"sd": 0.8366600265340756,
"se": 0.3741657386773941
}"""
Fintech Math Bootcamp · Lesson 076 of 120
Standard Error
Module 08: Sampling, Estimation, and Statistical Inference
Scenario: Measuring uncertainty in a mean rather than variation among payments
Rule: estimated SE(mean) = sample SD / √n
Try it: Which is the larger number here: sample SD or mean SE?
Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/sampling-estimation-and-statistical-inference/standard-error/
Free course: https://courses.thefintechbuilder.com
Synthetic teaching example, not financial advice or a production library.
"""
import json
import math
def lesson_076():
x = [2, 3, 3, 4, 4]
total = 0
for v in x:
total += v
mean = total / len(x)
squares = 0
for v in x:
squares += (v - mean) ** 2
variance = squares / (len(x) - 1)
return {"mean": mean, "sd": math.sqrt(variance), "se": math.sqrt(variance / len(x))}
if __name__ == "__main__":
print(json.dumps(lesson_076(), indent=2))
Your output
Press Run to execute the code in your browser.
Expected output
{
"mean": 3.2,
"sd": 0.8366600265340756,
"se": 0.3741657386773941
}// Fintech Math Bootcamp - Lesson 076 of 120
// Standard Error
// Module 08: Sampling, Estimation, and Statistical Inference
//
// Scenario: Measuring uncertainty in a mean rather than variation among payments
// Rule: estimated SE(mean) = sample SD / sqrt(n)
//
// Try it: Which is the larger number here: sample SD or mean SE?
//
// Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/sampling-estimation-and-statistical-inference/standard-error/
// Free course: https://courses.thefintechbuilder.com
// Synthetic teaching example, not financial advice or a production library.
import java.util.ArrayList;
import java.util.Arrays;
import java.util.LinkedHashMap;
import java.util.List;
import java.util.Map;
public class Main {
static Map<String, Object> lesson076() {
double[] x = {2, 3, 3, 4, 4};
double total = 0;
for (double v : x) total += v;
double mean = total / x.length;
double squares = 0;
for (double v : x) squares += Math.pow(v - mean, 2);
double variance = squares / (x.length - 1);
Map<String, Object> result = new LinkedHashMap<String, Object>();
result.put("mean", mean);
result.put("sd", Math.sqrt(variance));
result.put("se", Math.sqrt(variance / x.length));
return result;
}
public static void main(String[] args) {
System.out.println(toJson(lesson076(), ""));
}
// Minimal JSON writer: two-space indent, whole numbers without a decimal point, NaN as null.
static String toJson(Object value, String indent) {
if (value == null) return "null";
if (value instanceof Boolean) return value.toString();
if (value instanceof Number) return formatNumber(((Number) value).doubleValue());
if (value instanceof String) return quote((String) value);
if (value instanceof double[]) {
List<Object> boxed = new ArrayList<Object>();
for (double d : (double[]) value) boxed.add(d);
return toJson(boxed, indent);
}
if (value instanceof Object[]) return toJson(Arrays.asList((Object[]) value), indent);
String inner = indent + " ";
StringBuilder out = new StringBuilder();
if (value instanceof Map) {
Map<?, ?> map = (Map<?, ?>) value;
if (map.isEmpty()) return "{}";
out.append("{\n");
int i = 0;
for (Map.Entry<?, ?> entry : map.entrySet()) {
out.append(inner).append(quote(entry.getKey().toString())).append(": ")
.append(toJson(entry.getValue(), inner));
out.append(++i < map.size() ? ",\n" : "\n");
}
return out.append(indent).append("}").toString();
}
List<?> list = (List<?>) value;
if (list.isEmpty()) return "[]";
out.append("[\n");
for (int i = 0; i < list.size(); i++) {
out.append(inner).append(toJson(list.get(i), inner));
out.append(i + 1 < list.size() ? ",\n" : "\n");
}
return out.append(indent).append("]").toString();
}
static String formatNumber(double x) {
if (Double.isNaN(x) || Double.isInfinite(x)) return "null";
if (x == Math.rint(x) && Math.abs(x) < 1e15) return Long.toString((long) x);
return Double.toString(x);
}
static String quote(String s) {
StringBuilder out = new StringBuilder("\"");
for (char c : s.toCharArray()) {
if (c == '"' || c == '\\') out.append('\\').append(c);
else if (c == '\n') out.append("\\n");
else if (c < 0x20) out.append(String.format("\\u%04x", (int) c));
else out.append(c);
}
return out.append('"').toString();
}
}
No browser runner for Java yet
Read the code here, then run it in your own toolchain or a ready-made cloud workspace.
Expected output
{
"mean": 3.2,
"sd": 0.8366600265340756,
"se": 0.3741657386773941
}// Fintech Math Bootcamp · Lesson 076 of 120
// Standard Error
// Module 08: Sampling, Estimation, and Statistical Inference
//
// Scenario: Measuring uncertainty in a mean rather than variation among payments
// Rule: estimated SE(mean) = sample SD / √n
//
// Try it: Which is the larger number here: sample SD or mean SE?
//
// Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/sampling-estimation-and-statistical-inference/standard-error/
// Free course: https://courses.thefintechbuilder.com
// Synthetic teaching example, not financial advice or a production library.
package main
import (
"encoding/json"
"fmt"
"math"
)
type Lesson076Result struct {
Mean float64 `json:"mean"`
SD float64 `json:"sd"`
SE float64 `json:"se"`
}
func lesson076() Lesson076Result {
x := []float64{2, 3, 3, 4, 4}
n := float64(len(x))
total := 0.0
for _, v := range x {
total += v
}
mean := total / n
squares := 0.0
for _, v := range x {
squares += math.Pow(v-mean, 2)
}
variance := squares / (n - 1)
return Lesson076Result{Mean: mean, SD: math.Sqrt(variance), SE: math.Sqrt(variance / n)}
}
func main() {
out, err := json.MarshalIndent(lesson076(), "", " ")
if err != nil {
panic(err)
}
fmt.Println(string(out))
}
No browser runner for Go yet
Read the code here, then run it in your own toolchain or a ready-made cloud workspace.
Expected output
{
"mean": 3.2,
"sd": 0.8366600265340756,
"se": 0.3741657386773941
}// Fintech Math Bootcamp · Lesson 076 of 120
// Standard Error
// Module 08: Sampling, Estimation, and Statistical Inference
//
// Scenario: Measuring uncertainty in a mean rather than variation among payments
// Rule: estimated SE(mean) = sample SD / √n
//
// Try it: Which is the larger number here: sample SD or mean SE?
//
// Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/sampling-estimation-and-statistical-inference/standard-error/
// Free course: https://courses.thefintechbuilder.com
// Synthetic teaching example, not financial advice or a production library.
#include <cmath>
#include <cstdio>
#include <cstdlib>
#include <iostream>
#include <optional>
#include <stdexcept>
#include <string>
#include <utility>
#include <vector>
// A minimal JSON value, enough to print this lesson's result.
struct Json {
enum class Kind { Null, Bool, Number, String, Array, Object };
Kind kind = Kind::Null;
bool flag = false;
double number = 0.0;
std::string text;
std::vector<std::string> keys; // object keys, parallel to items
std::vector<Json> items; // array elements or object values
Json() = default;
Json(bool value) : kind(Kind::Bool), flag(value) {}
Json(int value) : kind(Kind::Number), number(value) {}
Json(double value) : kind(Kind::Number), number(value) {}
Json(const char* value) : kind(Kind::String), text(value) {}
Json(const std::string& value) : kind(Kind::String), text(value) {}
Json(const std::vector<double>& values) : kind(Kind::Array) {
for (double v : values) items.push_back(Json(v));
}
};
Json jsonArray(const std::vector<Json>& values) {
Json array;
array.kind = Json::Kind::Array;
array.items = values;
return array;
}
Json jsonObject(const std::vector<std::pair<std::string, Json>>& fields) {
Json object;
object.kind = Json::Kind::Object;
for (const auto& field : fields) {
object.keys.push_back(field.first);
object.items.push_back(field.second);
}
return object;
}
// Shortest decimal form that reads back as the same double.
std::string formatNumber(double x) {
if (!std::isfinite(x)) return "null";
char buffer[32];
if (x == std::floor(x) && std::fabs(x) < 1e15) {
std::snprintf(buffer, sizeof buffer, "%.0f", x);
return buffer;
}
for (int precision = 1; precision <= 17; ++precision) {
std::snprintf(buffer, sizeof buffer, "%.*g", precision, x);
if (std::strtod(buffer, nullptr) == x) break;
}
return buffer;
}
std::string quote(const std::string& s) {
std::string out = "\"";
for (char c : s) {
if (c == '"' || c == '\\') { out += '\\'; out += c; }
else if (c == '\n') out += "\\n";
else out += c;
}
return out + "\"";
}
std::string toJson(const Json& value, const std::string& indent = "") {
switch (value.kind) {
case Json::Kind::Null: return "null";
case Json::Kind::Bool: return value.flag ? "true" : "false";
case Json::Kind::Number: return formatNumber(value.number);
case Json::Kind::String: return quote(value.text);
default: break;
}
const bool isObject = value.kind == Json::Kind::Object;
if (value.items.empty()) return isObject ? "{}" : "[]";
const std::string inner = indent + " ";
std::string out = isObject ? "{\n" : "[\n";
for (std::size_t i = 0; i < value.items.size(); ++i) {
out += inner;
if (isObject) out += quote(value.keys[i]) + ": ";
out += toJson(value.items[i], inner);
out += i + 1 < value.items.size() ? ",\n" : "\n";
}
return out + indent + (isObject ? "}" : "]");
}
Json lesson076() {
const std::vector<double> x = {2, 3, 3, 4, 4};
const double n = static_cast<double>(x.size());
double total = 0.0;
for (double v : x) total += v;
const double mean = total / n;
double squares = 0.0;
for (double v : x) squares += std::pow(v - mean, 2);
const double variance = squares / (n - 1);
return jsonObject({
{"mean", mean},
{"sd", std::sqrt(variance)},
{"se", std::sqrt(variance / n)},
});
}
int main() {
std::cout << toJson(lesson076()) << '\n';
return 0;
}
No browser runner for C++ yet
Read the code here, then run it in your own toolchain or a ready-made cloud workspace.
Expected output
{
"mean": 3.2,
"sd": 0.8366600265340756,
"se": 0.3741657386773941
}// Fintech Math Bootcamp · Lesson 076 of 120
// Standard Error
// Module 08: Sampling, Estimation, and Statistical Inference
//
// Scenario: Measuring uncertainty in a mean rather than variation among payments
// Rule: estimated SE(mean) = sample SD / √n
//
// Try it: Which is the larger number here: sample SD or mean SE?
//
// Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/sampling-estimation-and-statistical-inference/standard-error/
// Free course: https://courses.thefintechbuilder.com
// Synthetic teaching example, not financial advice or a production library.
/// A minimal JSON value, enough to print this lesson's result.
#[allow(dead_code)]
enum Json {
Null,
Bool(bool),
Num(f64),
Str(String),
Arr(Vec<Json>),
Obj(Vec<(String, Json)>),
}
#[allow(dead_code)]
impl Json {
fn obj(fields: Vec<(&str, Json)>) -> Json {
Json::Obj(fields.into_iter().map(|(k, v)| (k.to_string(), v)).collect())
}
fn nums(values: &[f64]) -> Json {
Json::Arr(values.iter().map(|&v| Json::Num(v)).collect())
}
/// Pretty-prints with two-space indentation.
fn pretty(&self, indent: &str) -> String {
let inner = format!("{} ", indent);
match self {
Json::Null => "null".to_string(),
Json::Bool(b) => b.to_string(),
Json::Num(x) => format_number(*x),
Json::Str(s) => quote(s),
Json::Arr(items) if items.is_empty() => "[]".to_string(),
Json::Obj(fields) if fields.is_empty() => "{}".to_string(),
Json::Arr(items) => {
let body: Vec<String> = items
.iter()
.map(|v| format!("{}{}", inner, v.pretty(&inner)))
.collect();
format!("[\n{}\n{}]", body.join(",\n"), indent)
}
Json::Obj(fields) => {
let body: Vec<String> = fields
.iter()
.map(|(k, v)| format!("{}{}: {}", inner, quote(k), v.pretty(&inner)))
.collect();
format!("{{\n{}\n{}}}", body.join(",\n"), indent)
}
}
}
}
fn format_number(x: f64) -> String {
if !x.is_finite() {
"null".to_string()
} else if x == x.trunc() && x.abs() < 1e15 {
format!("{}", x as i64)
} else {
format!("{}", x)
}
}
fn quote(s: &str) -> String {
let mut out = String::from("\"");
for c in s.chars() {
match c {
'"' => out.push_str("\\\""),
'\\' => out.push_str("\\\\"),
'\n' => out.push_str("\\n"),
c => out.push(c),
}
}
out.push('"');
out
}
fn lesson_076() -> Json {
let x = [2.0_f64, 3.0, 3.0, 4.0, 4.0];
let n = x.len() as f64;
let mean = x.iter().fold(0.0_f64, |s, v| s + v) / n;
let variance: f64 = x.iter().fold(0.0_f64, |s, v| s + (v - mean).powf(2.0)) / (n - 1.0);
Json::obj(vec![
("mean", Json::Num(mean)),
("sd", Json::Num(variance.sqrt())),
("se", Json::Num((variance / n).sqrt())),
])
}
fn main() {
println!("{}", lesson_076().pretty(""));
}
No browser runner for Rust yet
Read the code here, then run it in your own toolchain or a ready-made cloud workspace.
Expected output
{
"mean": 3.2,
"sd": 0.8366600265340756,
"se": 0.3741657386773941
}// Fintech Math Bootcamp · Lesson 076 of 120
// Standard Error
// Module 08: Sampling, Estimation, and Statistical Inference
//
// Scenario: Measuring uncertainty in a mean rather than variation among payments
// Rule: estimated SE(mean) = sample SD / √n
//
// Try it: Which is the larger number here: sample SD or mean SE?
//
// Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/sampling-estimation-and-statistical-inference/standard-error/
// Free course: https://courses.thefintechbuilder.com
// Synthetic teaching example, not financial advice or a production library.
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text.Json;
var options = new JsonSerializerOptions { WriteIndented = true };
Console.WriteLine(JsonSerializer.Serialize(Lesson076(), options));
static object Lesson076()
{
double[] x = { 2, 3, 3, 4, 4 };
double mean = x.Aggregate(0.0, (s, v) => s + v) / x.Length;
double variance = x.Aggregate(0.0, (s, v) => s + Math.Pow(v - mean, 2)) / (x.Length - 1);
return new
{
mean,
sd = Math.Sqrt(variance),
se = Math.Sqrt(variance / x.Length),
};
}
No browser runner for C# yet
Read the code here, then run it in your own toolchain or a ready-made cloud workspace.
Expected output
{
"mean": 3.2,
"sd": 0.8366600265340756,
"se": 0.3741657386773941
}Prefer your own machine? Every file is in the course repository · open it in Codespaces.
Lesson notes
The rule
estimated SE(mean) = sample SD / √n