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

Confidence Intervals

Confidence Intervals

An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a sample proportion. However, unlike the point estimate which is a single value, the confidence interval contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A confidence...

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...

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...

Uncertainty: Confidence Intervals

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor 't,' or...

Confidence Coefficient

Confidence Coefficient

The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under both the...

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...

You might also read

Related Articles

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

Sort by

Same author

Precision of measurements with arbitrary confidence levels when samples are counted equal to or longer than blanks by computer codes.

Health physics·2015

Same author

Radon treatment controversy.

Dose-response : a publication of International Hormesis Society·2008

Same author

Measurements should not be recorded as less than MDA: an extension.

Health physics·2008

Same author

Facts and controversies about radiation exposure, part 1: controlling unnecessary radiation exposures.

Journal of the American College of Radiology : JACR·2007

Same author

Facts and controversies about radiation exposure, part 2: low-level exposures and cancer risk.

Journal of the American College of Radiology : JACR·2007

Same author

Disparities in adherence to recommended followup on screening mammography: interaction of sociodemographic factors.

Ethnicity & disease·2002

Search research articles

Related Experiment Video

Updated: Jun 3, 2026

Automated Quantification and Analysis of Cell Counting Procedures Using ImageJ Plugins

Automated Quantification and Analysis of Cell Counting Procedures Using ImageJ Plugins

Published on: November 17, 2016

Improved confidence intervals when the sample is counted an integer times longer than the blank.

William Edward Potter¹, Jadwiga Jodi Strzelczyk

¹University of Colorado Hospital, Aurora, CO 80045, USA. we_potter713@att.net

|April 1, 2011

Summary

This study extends confidence interval calculations for paired counting data, particularly when the ratio of sample to blank count time (IRR) is an integer. It provides a method for determining the probability density function for net counts in such scenarios.

More Related Videos

Digital PCR-based Competitive Index for High-throughput Analysis of Fitness in Salmonella

Digital PCR-based Competitive Index for High-throughput Analysis of Fitness in Salmonella

Published on: May 13, 2019

Fast Colony Forming Unit Counting in 96-Well Plate Format Applied to the Drosophila Microbiome

Fast Colony Forming Unit Counting in 96-Well Plate Format Applied to the Drosophila Microbiome

Published on: January 13, 2023

Related Experiment Videos

Last Updated: Jun 3, 2026

Automated Quantification and Analysis of Cell Counting Procedures Using ImageJ Plugins

Automated Quantification and Analysis of Cell Counting Procedures Using ImageJ Plugins

Published on: November 17, 2016

Digital PCR-based Competitive Index for High-throughput Analysis of Fitness in Salmonella

Digital PCR-based Competitive Index for High-throughput Analysis of Fitness in Salmonella

Published on: May 13, 2019

Fast Colony Forming Unit Counting in 96-Well Plate Format Applied to the Drosophila Microbiome

Fast Colony Forming Unit Counting in 96-Well Plate Format Applied to the Drosophila Microbiome

Published on: January 13, 2023

Area of Science:

Statistics
Nuclear Counting
Data Analysis

Background:

Accurate confidence intervals are crucial for paired counting data analysis.
Existing methods for confidence intervals in paired counting have limitations.
The ratio of sample count time to blank count time (IRR) is often an integer in practical applications.

Purpose of the Study:

To extend existing computer solutions for confidence intervals in paired counting.
To address scenarios where the integer ratio of sample count time to blank count time (IRR) is applicable.
To develop a method for calculating the probability density function (PDF) of net counts.

Main Methods:

The study extends past computational techniques for confidence intervals.
It employs a method similar to Pearson and Hartley's tabulation for Poisson and Binomial distributions.
Assumes blank counts and sample contributions to gross counts are Poisson distributed with a known expected blank count.

Main Results:

A method is presented for calculating confidence intervals in paired counting with an integer IRR.
The probability density function (PDF) for the net count (OC) is derived.
The approach clarifies the nomenclature of confidence intervals, favoring 'Neyman confidence intervals' or 'confidence intervals' over 'Neyman-Pearson confidence intervals'.

Conclusions:

The developed technique provides a robust method for confidence interval estimation in paired counting with integer IRR.
This work refines the understanding and application of statistical methods in counting experiments.
The straightforward derivation of the net count's PDF facilitates further statistical analysis.