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Estimating Population Standard Deviation

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...

Distributions to Estimate Population Parameter

Distributions to Estimate Population Parameter

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...

Confidence Interval for Estimating Population Mean

Confidence Interval for Estimating Population Mean

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...

Estimating Population Mean with Known Standard Deviation

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...

Estimating Population Mean with Unknown Standard Deviation

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...

Estimation of the Physical Quantities

Estimation of the Physical Quantities

On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...

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Assessment of Child Anthropometry in a Large Epidemiologic Study

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Quality assuring the 2011 Census population estimates.

Jonathan Wroth-Smith¹, Owen Abbott, Garnett Compton

¹Office for National Statistics.

Population Trends

|April 6, 2011

Summary

This summary is machine-generated.

The 2011 Census quality assurance ensures accurate population estimates. Rigorous methods and adjustments build user confidence in the census data results.

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Area of Science:

Demography
Statistical Science

Background:

Accurate population data is crucial for policy and resource allocation.
The 2011 Census provides a decennial snapshot of the population.
Ensuring the quality of census estimates is paramount for data reliability.

Purpose of the Study:

To detail the quality assurance (QA) methods for the 2011 Census population estimates.
To explain the processes and adjustments implemented for QA.
To underscore the importance of QA for user confidence in census data.

Main Methods:

Description of the comprehensive QA framework for census estimates.
Explanation of statistical adjustments and validation procedures.
Overview of the iterative processes involved in ensuring data accuracy.

Main Results:

The implemented QA methods successfully addressed potential sources of error.
Adjustments were made to refine population estimates.
The QA process confirmed the robustness of the census data.

Conclusions:

The quality assurance strategy for the 2011 Census was effective.
Robust population estimates were achieved through meticulous methods and adjustments.
High-quality census data enhances its utility for diverse users.