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Related Concept Videos

Estimating Population Standard Deviation01:26

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 Deviation01:22

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...
Population Growth00:57

Population Growth

Population size is dynamic, increasing with birth rates and immigration, and decreasing with death rates and emigration. In ideal conditions with unlimited resources, populations can increase exponentially, which plots as a J-shaped growth rate curve of population size against time. This type of curve is characteristic of newly-introduced invasive species, or populations that have suffered catastrophic declines and are rebounding.
Estimating Population Mean with Known Standard Deviation01:16

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...
Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
Actuarial Approach01:20

Actuarial Approach

The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...

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Related Experiment Video

Updated: May 31, 2026

Assessment of Child Anthropometry in a Large Epidemiologic Study
09:36

Assessment of Child Anthropometry in a Large Epidemiologic Study

Published on: February 2, 2017

Estimating gestational age at birth: a population-based derivation-validation study.

M L Urquia1, T A Stukel, K Fung

  • 1Centre for Research on Inner City Health, The Keenan Research Centre of the Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, Ontario, Canada.

Chronic Diseases and Injuries in Canada
|July 8, 2011
PubMed
Summary
This summary is machine-generated.

Gestational age (GA) prediction for newborns is crucial for infant health research. A model using birth weight and sex reasonably approximates GA for preterm infants, aiding epidemiological studies.

Related Experiment Videos

Last Updated: May 31, 2026

Assessment of Child Anthropometry in a Large Epidemiologic Study
09:36

Assessment of Child Anthropometry in a Large Epidemiologic Study

Published on: February 2, 2017

Area of Science:

  • Perinatal epidemiology
  • Neonatal research

Background:

  • Gestational age (GA) is vital for perinatal and infant health research.
  • Administrative databases often lack precise GA information.
  • Accurate GA data is essential for epidemiological studies.

Purpose of the Study:

  • To develop and validate a gestational age prediction model for singleton births.
  • To provide a tool for epidemiological studies lacking direct GA measurements.

Main Methods:

  • Linear regression analysis used for model derivation.
  • Data from 130,328 Ontario newborns (2007-2009) for model development.
  • Validation performed on a separate sample of 130,329 newborns.

Main Results:

  • The model, using birth weight and sex, showed reasonable accuracy for preterm infants (before 37 weeks, r2 = 0.67).
  • Model performance was poor for term births (37-42 weeks, r2 = 0.12).
  • Inclusion of additional maternal and infant characteristics did not enhance model discrimination.

Conclusions:

  • Gestational age before 37 weeks can be reliably estimated using birth weight and sex.
  • This model offers a practical solution for estimating GA in epidemiological research when direct data is unavailable.