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SDCalc
中級応用·11 min

標準偏差を使った信頼区間の構築

標準偏差を用いた信頼区間の構築方法を解説。信頼水準の意味と、実際の場面での信頼区間の解釈方法を学びましょう。

信頼区間とは?

信頼区間 (CI) とは、真の母集団パラメータが含まれる可能性が高い値の範囲です。1つの点推定値を示す代わりに、範囲を提示することで不確実性を認めます。

「真の平均が48.2から51.8の間にあると95%の確信がある」

95% CI: [48.2, 51.8]

公式

母集団平均の信頼区間は以下の通りです。

信頼区間の公式

CI = x̄ ± z* × (σ / √n)
  • x̄ = 標本平均
  • z* = 臨界値(95%信頼区間の場合は1.96)
  • σ = 標準偏差
  • n = 標本サイズ
  • σ/√n = 標準誤差
信頼水準z*の値
90%1.645
95%1.960
99%2.576

正しい解釈

よくある誤解

95%信頼区間は「真の平均がこの区間内にある確率が95%」という意味ではありません。真の平均はこの区間内にあるかないかのどちらかで、固定されています。

正しい解釈

「この標本抽出の手順を繰り返した場合、計算された区間の95%が真の母集団平均を含む」という意味です。

計算例

例:顧客満足度調査

100人の顧客を調査した結果、満足度スコアの平均は7.5、標準偏差は1.5でした。95%信頼区間を計算しましょう。
1

標準誤差を求める

SE = 1.5 / √100 = 0.15
2

誤差の範囲を計算する

ME = 1.96 × 0.15 = 0.294
3

区間を構築する

CI = 7.5 ± 0.294 = [7.21, 7.79]

解釈: 真の平均顧客満足度が7.21から7.79の間にあると、95%の確信を持てます。

信頼区間の幅に影響する要因

標本サイズ (n)

nが大きいほど = 狭い信頼区間 データが多いほど精度が高まる

標準偏差 (σ)

σが大きいほど = 広い信頼区間 ばらつきが大きいほど確実性が低下

信頼水準

信頼水準が高いほど = 広い信頼区間 99%信頼区間は95%信頼区間より広い

Further Reading

How to Read This Article

A statistics tutorial is a practical interpretation guide, not just a formula dump. It refers to the assumptions, notation, and reporting language that analysts need when they explain a result to a teacher, manager, client, or reviewer. The article body covers the specific topic, while the sections below create a common interpretation frame that readers can reuse across related metrics.

Reading goalWhat to focus onCommon mistake
DefinitionWhat the metric is and what quantity it summarizesTreating the formula as self-explanatory
Formula choiceSample versus population assumptions and notationUsing n when n-1 is required or vice versa
InterpretationWhether the result indicates concentration, spread, or riskCalling a large value good or bad without context

Frequently Asked Questions

How should I interpret a high standard deviation?

A high standard deviation means the observations are spread farther from the mean on average. Whether that spread is acceptable depends on the context: wide dispersion might signal risk in finance, instability in manufacturing, or genuine natural variation in scientific data.

Why do some articles mention n while others mention n-1?

The denominator reflects the difference between population and sample formulas. Population variance and population standard deviation use N because the full dataset is known. Sample variance and sample standard deviation often use n-1 because Bessel’s correction reduces bias when estimating population spread from a sample.

What is a statistical interpretation guide?

A statistical interpretation guide is a page that moves beyond arithmetic and explains meaning. It tells you what a metric is, when the formula applies, and how to describe the result in plain English without overstating certainty.

Can I cite this article in a report?

You should cite the underlying authoritative reference for formal work whenever possible. This page is best used as an explanatory bridge that helps you understand the concept before quoting the original standard or handbook.

Why include direct citations on every article page?

Direct citations give readers a route to verify the definition, notation, and assumptions. That improves trust and reduces the chance that a simplified explanation is mistaken for the entire technical standard.

Authoritative References

These sources define the concepts referenced most often across our articles. Bessel's correction is a sample adjustment, variance is a squared measure of spread, and standard deviation is the square root of variance expressed in the same units as the data.