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

Central Limit Theorem01:14

Central Limit Theorem

19.0K
The central limit theorem, abbreviated as clt, is one of the most powerful and useful ideas in all of statistics. The central limit theorem for sample means says that if you repeatedly draw samples of a given size and calculate their means, and create a histogram of those means, then the resulting histogram will tend to have an approximate normal bell shape. In other words, as sample sizes increase, the distribution of means follows the normal distribution more closely.
The sample size, n, that...
19.0K
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.2K
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...
3.2K
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

9.4K
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 +...
9.4K
Variation: Normal Distribution, Range, and Standard Deviation02:32

Variation: Normal Distribution, Range, and Standard Deviation

25.6K
In the field of psychology, there are several ways to organize measurements of a trait, feature, or characteristic (i.e., variables). Qualitative data, such as ethnicity, can be tabulated into a frequency count to provide information about the proportion, as well as the variety of groups in a sample or population. On the other hand, researchers can perform a wider set of calculations on quantitative data. The mean, mode, and median, for instance, are central tendency measures to identify a...
25.6K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.6K
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...
8.6K
Systematic Sampling Method01:17

Systematic Sampling Method

12.2K
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...
12.2K

You might also read

Related Articles

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

Sort by
Same author

Capturing infant and child growth dynamics with P-splines mixed effects models.

International journal of obesity (2005)·2026
Same author

Comparing Weightlifting Performances of Masters Athletes Across Age, Body Mass, and Sex From 2000 to 2025.

International journal of sports physiology and performance·2026
Same author

SITAR-d: extending the SITAR growth curve model to allow for variability in post-pubertal velocity.

Annals of human biology·2026
Same author

Forty years later: adult health and non-communicable disease following the 1984-1985 Great Ethiopian Famine - a retrospective cohort study.

BMJ global health·2026
Same author

The decline of child stunting in 122 countries: a systematic review of child growth studies since the 19th century.

BMJ global health·2026
Same author

Improved Centile Estimation by Transformation And/Or Adaptive Smoothing of the Explanatory Variable.

Statistics in medicine·2026
Same journal

A joint model for a longitudinal outcome and a progressive multistate model under a mixed observation scheme.

Statistical methods in medical research·2026
Same journal

Efficient semi-supervised estimation of optimal individualized treatment regimes with survival outcome.

Statistical methods in medical research·2026
Same journal

Asymptotic online FWER control for dependent test statistics.

Statistical methods in medical research·2026
Same journal

Regression analysis of misclassified current status data with potentially unknown test accuracy.

Statistical methods in medical research·2026
Same journal

Bayesian multivariate linear mixed-effects models with varied association structures.

Statistical methods in medical research·2026
Same journal

Inference about the ratio of age-standardized rates between two overlapping populations.

Statistical methods in medical research·2026
See all related articles

Related Experiment Video

Updated: Dec 6, 2025

Sampling Strategies and Processing of Biobank Tissue Samples from Porcine Biomedical Models
05:07

Sampling Strategies and Processing of Biobank Tissue Samples from Porcine Biomedical Models

Published on: March 6, 2018

16.0K

Sample size and sample composition for constructing growth reference centiles.

T J Cole1

  • 1UCL Great Ormond Street Institute of Child Health, London, UK.

Statistical Methods in Medical Research
|October 12, 2020
PubMed
Summary
This summary is machine-generated.

Optimizing sample size and age distribution is crucial for accurate child growth reference charts. Studies suggest 7,000-25,000 subjects per sex are needed for reliable centile estimation.

Keywords:
GAMLSSGrowth referenceLMS methodanthropometrysample size

More Related Videos

Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

35.6K
Precise, High-throughput Analysis of Bacterial Growth
09:00

Precise, High-throughput Analysis of Bacterial Growth

Published on: September 19, 2017

24.6K

Related Experiment Videos

Last Updated: Dec 6, 2025

Sampling Strategies and Processing of Biobank Tissue Samples from Porcine Biomedical Models
05:07

Sampling Strategies and Processing of Biobank Tissue Samples from Porcine Biomedical Models

Published on: March 6, 2018

16.0K
Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

35.6K
Precise, High-throughput Analysis of Bacterial Growth
09:00

Precise, High-throughput Analysis of Bacterial Growth

Published on: September 19, 2017

24.6K

Area of Science:

  • Pediatrics
  • Biostatistics
  • Growth Monitoring

Background:

  • Growth reference centile charts are essential tools in child health assessment.
  • Current study designs for collecting reference data lack clear guidance, leading to variable sample sizes.
  • Methods like the LMS method and GAMLSS are used for centile construction.

Purpose of the Study:

  • To establish a theoretical framework for optimal design of cross-sectional growth reference studies.
  • To determine the ideal sample size and age distribution for precise centile estimation.
  • To provide practical guidance for future growth study designs.

Main Methods:

  • Utilized Generalised Additive Models for Location Scale and Shape (GAMLSS) to fit centiles for weight, height, BMI, and head circumference.
  • Analyzed data from 6878 boys aged 0-21 years from the Fourth Dutch Growth Study.
  • Employed simulation studies to explore the impact of sample size and age distribution (sample composition) on precision.

Main Results:

  • Smoothing centiles increased effective sample size two- to threefold through 'borrowing strength'.
  • Optimal sample composition varied by centile: λ=0.4 for the median (infant over-sampling) and λ=0.75 for the 2nd/98th centiles (less infant over-sampling).
  • Precision was measured on the z-score scale, with standard errors of 0.041 for the median and 0.066 for extreme centiles.

Conclusions:

  • Both sample size and sample composition are critical factors for optimal growth reference study design.
  • Well-designed studies require 7,000-25,000 subjects per sex for accurate growth assessment.
  • The findings offer practical recommendations for improving the methodology of growth reference studies.