Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.4K
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...
3.4K
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

9.7K
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 +...
9.7K
Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

8.9K
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...
8.9K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

5.1K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
5.1K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.9K
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...
8.9K
Conservation of Small Populations02:04

Conservation of Small Populations

17.3K
Small population sizes put a species at extreme risk of extinction due to a lack of variation, and a consequent decrease in adaptability. This weakens the chances of survival under pressures such as climate change, competition from other species, or new diseases. Large populations are more likely to survive pressures such as these, as such populations are more likely to harbor individuals that have genetic variants that are adaptive under new stresses. Small populations are much less...
17.3K

You might also read

Related Articles

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

Sort by
Same author

The benefits of Shuai Shou Gong (SSG) demonstrated in a Randomised Control Trial (RCT) study of older adults in two communities in Thailand.

PloS one·2023
Same author

Eastern Canada Flocks: images and manually annotated bird positions.

Ecology·2021
Same author

Epilepsy and Electroencephalographic Abnormalities in SATB2-Associated Syndrome.

Pediatric neurology·2020
Same author

Robotic Extrusion of Algae-Laden Hydrogels for Large-Scale Applications.

Global challenges (Hoboken, NJ)·2020
Same author

Evaluating the pharmacological response in fluorescence microscopy images: The Δm algorithm.

PloS one·2019
Same author

Simplified procedure for efficient and unbiased population size estimation.

PloS one·2018

Related Experiment Video

Updated: Feb 2, 2026

Topographical Estimation of Visual Population Receptive Fields by fMRI
06:02

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

9.7K

Correction: Simplified procedure for efficient and unbiased population size estimation.

Marcos Cruz, Javier González-Villa

    Plos One
    |November 27, 2018
    PubMed
    Summary

    This study corrects a previous article DOI. The corrected DOI is 10.1371/journal.pone.0206091, ensuring proper citation and access to the research findings.

    Area of Science:

    • Scientific Publishing
    • Bibliometrics
    • Scholarly Communication

    Background:

    • Accurate citation is crucial for scientific integrity.
    • Digital Object Identifiers (DOIs) are essential for locating research articles.
    • Errors in DOIs can hinder research accessibility and impact.

    Purpose of the Study:

    • To correct a previously published article DOI.
    • To ensure accurate referencing for the research.
    • To facilitate proper retrieval of the scientific work.

    Main Methods:

    • Identification of the erroneous DOI.
    • Verification of the correct DOI.
    • Publication of a correction notice.

    Main Results:

    More Related Videos

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
    12:27

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

    Published on: February 15, 2017

    7.4K
    Knowing What Counts: Unbiased Stereology in the Non-human Primate Brain
    11:25

    Knowing What Counts: Unbiased Stereology in the Non-human Primate Brain

    Published on: May 14, 2009

    14.3K

    Related Experiment Videos

    Last Updated: Feb 2, 2026

    Topographical Estimation of Visual Population Receptive Fields by fMRI
    06:02

    Topographical Estimation of Visual Population Receptive Fields by fMRI

    Published on: February 3, 2015

    9.7K
    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
    12:27

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

    Published on: February 15, 2017

    7.4K
    Knowing What Counts: Unbiased Stereology in the Non-human Primate Brain
    11:25

    Knowing What Counts: Unbiased Stereology in the Non-human Primate Brain

    Published on: May 14, 2009

    14.3K
    • The incorrect DOI has been identified and flagged.
    • The correct DOI, 10.1371/journal.pone.0206091, is now provided.
    • The article is now accurately citable and accessible.

    Conclusions:

    • Correction of DOIs is a vital process in scholarly publishing.
    • Ensuring DOI accuracy upholds the reliability of scientific records.
    • This correction enhances the discoverability and citation of the research.