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Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

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

Estimating Population Mean with Known Standard Deviation

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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 +...
9.6K
Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

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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...
8.8K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

5.0K
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...
5.0K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.8K
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...
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The Nernst Equation02:59

The Nernst Equation

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Nonstandard Reaction Conditions
The interconnection between standard cell potentials and various thermodynamic parameters such as the standard free energy change ΔG° and equilibrium constant K has been previously explored. For example, a redox reaction involving zinc(II) and tin(II) ions at 1 M concentration with Eºcell = +0.291 V and ΔG° = −56.2 kJ is spontaneous.
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Related Experiment Video

Updated: Jan 21, 2026

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

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Age estimation equations using vertebral osteophyte formation in a Thai population: comparison and modified

Sithee Praneatpolgrang1,2, Sukon Prasitwattanaseree3,4, Pasuk Mahakkanukrauh2,4

  • 1Forensic Osteology Research Center, Faculty of Medicine and Graduate School, Chiang Mai University, Chiang Mai, Thailand.

Anatomy & Cell Biology
|July 25, 2019
PubMed
Summary

This study developed age estimation equations for the Thai population using vertebral osteophytes. The new method provides reliable age assessment for forensic identification cases.

Keywords:
Age estimationForensic anthropologyForensic scienceThailandVertebral osteophytes

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Area of Science:

  • Forensic Anthropology
  • Osteology
  • Biological Identification

Background:

  • Age estimation from skeletal remains is crucial in forensic science.
  • Vertebral osteophytes are indicators of aging.
  • Previous methods require refinement for specific populations.

Purpose of the Study:

  • To develop a new age estimation equation for the Thai population.
  • To utilize vertebral osteophytes for age assessment.
  • To provide accurate age estimation for forensic cases.

Main Methods:

  • Scoring osteophyte formation on cervical, thoracic, and lumbar vertebrae (C2-L5).
  • Applying established criteria and a new modified score based on osteophyte length.
  • Analyzing data from 400 Thai individuals (22-97 years old).

Main Results:

  • High correlation coefficients (R-squared up to 0.642) were found for age estimation using lumbar vertebrae.
  • Statistically significant results (P<0.01) support the reliability of the developed equations.
  • Equations were generated for males, females, and combined sexes.

Conclusions:

  • Vertebral osteophytes offer a viable method for age estimation in the Thai population.
  • The developed equations can be applied in forensic investigations.
  • This research contributes to accurate biological identification in forensic cases.