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

Sample Size Calculation01:19

Sample Size Calculation

3.2K
Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
3.2K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.0K
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...
4.0K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

7.6K
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...
7.6K
Testing a Claim about Mean: Unknown Population SD01:21

Testing a Claim about Mean: Unknown Population SD

3.4K
A complete procedure of testing a hypothesis about a population mean when the population standard deviation is unknown is explained here.
Estimating a population mean requires the samples to be approximately normally distributed. The data should be collected from the randomly selected samples having no sampling bias. There is no specific requirement for sample size. But if the sample size is less than 30, and we don't know the population standard deviation, a different approach is used;...
3.4K
Systematic Sampling Method01:17

Systematic Sampling Method

10.0K
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. Data are the result of sampling from a 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.
Systematic sampling is one of the simplest methods...
10.0K
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.0K
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...
3.0K

You might also read

Related Articles

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

Sort by
Same author

Idiopathic Pulmonary Fibrosis update, comparing the Australasian Interstitial Lung Disease Registry to the Australian Idiopathic Pulmonary Fibrosis Registry.

Internal medicine journal·2026
Same author

Analysing complex interventions using component network meta-analysis.

BMJ (Clinical research ed.)·2026
Same author

Visualization of Multi-indication Randomized Control Trial Evidence to Support Decision Making in Oncology: A Case Study on Bevacizumab.

Medical decision making : an international journal of the Society for Medical Decision Making·2026
Same author

Methods for Evaluation of Surrogate Endpoints for Health Technology Assessment Decision Making: A Good Practices Report of an ISPOR Task Force.

Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research·2026
Same author

Methods of multi-indication meta-analysis for health technology assessment: A simulation study.

Research synthesis methods·2026
Same author

Methods for information-sharing in network meta-analysis: Implications for inference and policy.

Research synthesis methods·2026

Related Experiment Video

Updated: Jun 7, 2025

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

8.7K

Three new methodologies for calculating the effective sample size when performing population adjustment.

Landan Zhang1, Sylwia Bujkiewicz2, Dan Jackson3

  • 1Medical Affairs Statistics, Bayer plc, 400 S Oak Way, Reading, RG2 6AD, UK.

BMC Medical Research Methodology
|November 20, 2024
PubMed
Summary
This summary is machine-generated.

New methods for calculating effective sample size (ESS) improve statistical analysis when samples aren't representative. These approaches offer greater accuracy for weighted data, enhancing epidemiological and statistical inferences.

Keywords:
Indirect treatment comparisonsInverse probability weightingPropensity scoreSurvey weightsWeighted statistical analysis

More Related Videos

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.4K
Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems
07:41

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems

Published on: July 30, 2019

7.4K

Related Experiment Videos

Last Updated: Jun 7, 2025

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

8.7K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.4K
Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems
07:41

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems

Published on: July 30, 2019

7.4K

Area of Science:

  • Epidemiology
  • Statistics
  • Statistical Inference

Background:

  • The concept of population is crucial in epidemiology and statistics.
  • Weighting is used for inferences when samples are not representative of the population of interest.
  • Effective sample size (ESS) quantifies information retained after weighting data.

Purpose of the Study:

  • To propose novel methods for computing the effective sample size (ESS).
  • To provide ESS calculation approaches valid for any data type and weighted analysis.
  • To enhance the precision and applicability of ESS in statistical practice.

Main Methods:

  • Developed three new computational approaches for ESS.
  • Ensured proposed methods are valid for diverse data types, including non-homoscedastic data.
  • Focused on general applicability beyond traditional assumptions.

Main Results:

  • The proposed ESS calculation methods are broadly applicable.
  • These new approaches address limitations of conventional ESS formulas, particularly for non-homoscedastic data.
  • Demonstrated the utility of the new methods through practical examples.

Conclusions:

  • The novel ESS computation methods offer a more robust alternative to existing approaches.
  • These proposals should be considered for accompanying or replacing current ESS calculation practices.
  • Enhanced accuracy and broader applicability of ESS are key benefits.