• Confidence Intervals of a 1 Sample Problem are inference on parameter(s) of a single population
• 2-sample problems - inference on parameters involving two populations

CI - General Formula

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CI Definition

An interval [𝐴, 𝐵] is a (1 − 𝛼)100% confidence interval for the parameter 𝜃 if it contains the parameter with probability (1 − 𝛼):

• 𝐏{𝐴 ≤ 𝜃 ≤ 𝐵} = 1 − 𝛼

where:

• 𝛼 - significance level
• (1 − 𝛼) - confidence level or coverage probability

CI Formula Intuition

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Given a sample of data and a desired confidence level (1 − 𝛼), how can we construct a confidence interval [𝐴, 𝐵] that will satisfy the coverage condition

• 𝐏{𝐴 ≤ 𝜃 ≤ 𝐵} = 1 − 𝛼

first we need to estimate 𝜃

choose an unbiased estimator with normal distribution (e.g. MLE)

use the estimator to take the sample data and estimate 𝜃 point estimate 𝜃ˆ

next we standardize 𝜃ˆ to get a standard normal variable 𝑧:

• 𝑧 = [𝜃ˆ - 𝐄(𝜃ˆ)] / 𝜎(𝜃ˆ)

since 𝜃ˆ was estimated with an unbiased estimator: 𝐄(𝜃ˆ) = 𝜃

• 𝑧 = (𝜃ˆ - 𝜃) / 𝜎(𝜃ˆ)

this variable 𝑧 falls between the standard normal quantiles 𝑞_𝛼/2and 𝑞_1−𝛼/2, denoted by

• -𝑧_𝛼/2= 𝑞_𝛼/2
• 𝑧_𝛼/2= 𝑞_1−𝛼/2

with probability (1 - 𝛼), then:

• 𝐏{-𝑧_𝛼/2 ≤ (𝜃ˆ - 𝜃) / 𝜎(𝜃ˆ) ≤ 𝑧_𝛼/2} = 1 - 𝛼

now rearrange for 𝜃:

• 𝐏{𝜃ˆ - 𝑧_𝛼/2·𝜎(𝜃ˆ) ≤ 𝜃 ≤ 𝜃ˆ + 𝑧_𝛼/2·𝜎(𝜃ˆ)} = 1 - 𝛼

we have obtained two numbers:

• 𝐴 = 𝜃ˆ - 𝑧_𝛼/2·𝜎(𝜃ˆ)
• 𝐵 = 𝜃ˆ + 𝑧_𝛼/2·𝜎(𝜃ˆ)

such that

• 𝐏{𝐴 ≤ 𝜃 ≤ 𝐵} = 1 − 𝛼

CI Formulas

Large Sample Size (𝑛)	Normal Population	𝑆𝐸(𝜃ˆ) Known	Confidence Interval
FALSE	FALSE	EITHER	Bootstrap Method
FALSE	TRUE	FALSE	𝜃ˆ ± 𝑡𝛼/2·𝑆𝐸ˆ(𝜃ˆ)
FALSE	TRUE	TRUE	𝜃ˆ ± 𝑧𝛼/2·𝑆𝐸(𝜃ˆ)
TRUE	EITHER	FALSE	𝜃ˆ ± 𝑧𝛼/2·𝑆𝐸ˆ(𝜃ˆ)
TRUE	EITHER	TRUE	𝜃ˆ ± 𝑧𝛼/2·𝑆𝐸(𝜃ˆ)

where:

• 𝜃ˆ - point estimate/statistic or center of the interval
• 𝑧 - z-score a type of confidence multiplier
• 𝑡 - t-score a type of confidence multiplier
• 𝑆𝐸(𝜃ˆ) or 𝜎(𝜃ˆ) or 𝑆𝑡𝑑(𝜃ˆ) - standard error of the point estimator/statistic
• 𝑆𝐸ˆ(𝜃ˆ) or 𝑠(𝜃ˆ) or 𝑆𝑡𝑑ˆ(𝜃ˆ) - estimated standard error of the point estimator/statistic

CI Annotated

CI Diagram

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Subpages

• CI - Mean
• CI - Proportion
• CI - Variance & Standard Deviation