Confidence Interval: Estimation Rule in Statistics

Confidence Interval is an estimation rule that, with a given probability, provides intervals containing the true value of an unknown parameter when applied to repeated samples.

Definition§

A Confidence Interval (CI) is an estimation rule that, with a given probability, provides intervals containing the true value of an unknown parameter when applied to repeated samples. Essentially, if a large number of samples were drawn from the same population, and an x-per cent confidence interval was constructed for each sample, then about x per cent of these confidence intervals would contain the true value of the estimated parameter.

Historical Context§

The concept of the confidence interval was introduced by Jerzy Neyman in 1937. Neyman introduced a formal definition of the confidence interval in the context of hypothesis testing and parameter estimation. His contribution laid the groundwork for frequentist inference and has since become a standard method in statistical analysis.

Types/Categories§

• Single Sample Confidence Interval: Used for estimating the parameter (mean or proportion) of a single population.
• Paired Sample Confidence Interval: Used for estimating the mean difference between paired observations.
• Two-Sample Confidence Interval: Used for estimating the difference between two population parameters.
• Population Proportion Confidence Interval: Used to estimate the population proportion.

Key Events§

• 1937: Introduction by Jerzy Neyman.
• Mid-20th Century: Widespread adoption in statistical practices.
• 21st Century: Enhanced computational methods allow for more complex confidence intervals in big data analytics.

Detailed Explanations§

Mathematical Formulation§

For a population mean $\mu$ with a known or large sample size, the formula for a confidence interval is:

$$CI = \bar{x} \pm Z \left( \frac{\sigma}{\sqrt{n}} \right)$$

Where:

• $\bar{x}$ is the sample mean.
• $Z$ is the Z-score corresponding to the desired confidence level.
• $\sigma$ is the population standard deviation.
• $n$ is the sample size.

Confidence Level§

The confidence level represents the percentage of all possible samples that can be expected to include the true population parameter. Common confidence levels are 90%, 95%, and 99%.

Application and Importance§

Confidence intervals are critical in hypothesis testing, survey results, clinical trials, and any statistical analysis requiring estimation of population parameters. They provide a range of plausible values for the parameter, offering insights into the precision and reliability of the estimate.

Charts and Diagrams§

Examples§

1. Single Sample Mean: Constructing a 95% confidence interval for the mean height of a sample of people.
2. Proportion: Estimating the confidence interval for the proportion of voters favoring a candidate.

Considerations§

• Sample Size: Larger samples provide more accurate confidence intervals.
• Data Normality: Confidence intervals assume normality in the data distribution.
• Confidence Level Selection: Higher confidence levels yield wider intervals, offering more assurance but less precision.

Related Terms§

• Margin of Error: The range of values above and below the sample statistic in a confidence interval.
• P-value: Used to determine the significance in hypothesis testing.
• Standard Deviation: A measure of data dispersion around the mean.

Comparisons§

• Confidence Interval vs. Prediction Interval: Confidence intervals estimate a population parameter, while prediction intervals predict individual observations.
• Confidence Interval vs. Credible Interval: Used in Bayesian statistics to represent probability distributions.

Interesting Facts§

• Neyman’s introduction of confidence intervals revolutionized statistical inference, providing a means to quantify uncertainty.
• Confidence intervals are a key tool in polling and market research.

Inspirational Stories§

Dr. Jane Doe used confidence intervals to estimate the efficacy of a new drug, leading to its successful approval and use in treating millions of patients worldwide.

Famous Quotes§

“An approximate answer to the right problem is worth a good deal more than an exact answer to an approximate problem.” - John Tukey

Proverbs and Clichés§

• “Close enough for government work.”
• “Within a stone’s throw.”

Jargon and Slang§

• CI: Abbreviation for Confidence Interval.
• MOE: Margin of Error, part of a confidence interval calculation.

FAQs§

What is a confidence interval used for?

Confidence intervals are used to estimate the range within which a population parameter lies, based on sample data.

How do you interpret a 95% confidence interval?

A 95% confidence interval means that if we were to take 100 different samples and compute a confidence interval for each sample, we expect about 95 of the intervals to contain the true population parameter.

Why are confidence intervals important?

Confidence intervals provide a range of plausible values for an unknown parameter, offering insights into the precision and reliability of the estimate.

References§

• Neyman, J. (1937). “Outline of a Theory of Statistical Estimation Based on the Classical Theory of Probability”. Philosophical Transactions of the Royal Society of London.
• Bluman, A. G. (2017). “Elementary Statistics: A Step-by-Step Approach”. McGraw-Hill Education.

Summary§

Confidence intervals are a crucial tool in statistical analysis, providing a range of values within which the true parameter is likely to fall. Introduced by Jerzy Neyman, these intervals offer insights into the precision and reliability of estimates, making them indispensable in research, business, and policy-making.