Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Estimating Population Standard Deviation

Estimating Population Standard Deviation

When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...

Estimating Population Mean with Unknown Standard Deviation

Estimating Population Mean with Unknown Standard Deviation

In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the Guinness...

Estimating Population Mean with Known Standard Deviation

Estimating Population Mean with Known Standard Deviation

To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate + error bound)
The...

Interpretation of Confidence Intervals

Interpretation of Confidence Intervals

A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...

Stratified Sampling Method

Stratified Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...

Confidence Interval for Estimating Population Mean

Confidence Interval for Estimating Population Mean

A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

A note on point estimation and interval estimation of the relative treatment effect under a simple crossover design.

Pharmaceutical statistics·2021

Same author

Urinary metabolites of furan in waterpipe tobacco smokers compared to non-smokers in home settings in the US.

Toxicology letters·2020

Same author

Exact Confidence Limits on Some New Measures of Concordance and Discordance in Binary Outcomes.

Therapeutic innovation & regulatory science·2020

Same author

Exact interval estimators for some commonly used measures of binary agreement.

Statistics in medicine·2019

Same author

Asymptotic and exact interval estimators of the common odds ratio under the sequential parallel comparison design.

Statistical methods in medical research·2018

Same author

Exact and asymptotic tests under the sequential parallel comparison design.

Pharmaceutical statistics·2018

Search research articles

Related Experiment Video

Updated: May 22, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Notes on interval estimation of the gamma correlation under stratified random sampling.

Kung-Jong Lui¹, Kuang-Chao Chang

¹Department of Mathematics and Statistics, College of Sciences, San Diego State University, San Diego, CA 92182-7720, USA. kjl@rohan.sdsu.edu

Biometrical Journal. Biometrische Zeitschrift

|May 25, 2012

Summary

This study introduces four new interval estimators for gamma correlation in stratified sampling. Monte Carlo simulations show their performance and highlight potential accuracy loss when ignoring stratification.

More Related Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

Related Experiment Videos

Last Updated: May 22, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

Area of Science:

Statistics
Biostatistics
Social Sciences Research Methods

Background:

Stratified random sampling is crucial for accurate statistical inference.
Estimating gamma correlation in stratified samples requires robust interval estimators.
Existing methods may lose accuracy when stratification is ignored.

Purpose of the Study:

To develop and evaluate four novel asymptotic interval estimators for gamma correlation under stratified random sampling.
To compare the finite-sample performance of these new estimators against existing methods.
To assess the accuracy implications of using pooled data without accounting for stratification.

Main Methods:

Development of four closed-form asymptotic interval estimators: CIWLS, CIMHT, CIFT, and MWLSLR.
Monte Carlo simulation to evaluate finite-sample performance.
Application to real-world survey data on income and job satisfaction, stratified by gender.

Main Results:

The study presents four distinct interval estimators for gamma correlation in stratified samples.
Monte Carlo simulations provide insights into the finite-sample performance of these estimators.
The research demonstrates potential accuracy issues with pooled data analysis that disregards stratification.

Conclusions:

The developed interval estimators offer new tools for analyzing gamma correlation in stratified data.
Understanding estimator performance is vital for accurate statistical analysis in stratified populations.
Accounting for stratification is essential to avoid potential biases and inaccuracies in interval estimation.