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Estimating Population Standard Deviation01:26

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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

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

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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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[Indirect methods to estimate hidden population: Second part].

Rocío Lorenzo Ortega1, José Pulido2,3,4, Ana Martínez-Santos5

  • 1Servicio de Medicina Preventiva. Hospital Virgen de la Victoria. Málaga. España.

Revista Espanola De Salud Publica
|July 11, 2019
PubMed
Summary
This summary is machine-generated.

Estimating the size of hidden populations, those with stigmatizing or illegal traits, is challenging. This study reviews indirect methods using incomplete data to accurately assess these groups, highlighting potential biases.

Keywords:
Data CollectionEpidemiologic StudyEpidemiological MonitoringHidden PopulationsSpain

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Hidden populations, characterized by stigmatizing or illegal attributes, pose significant challenges for direct enumeration.
  • Traditional survey methods are often ineffective for accurately determining the size or prevalence of these groups.
  • Indirect estimation methods, utilizing incomplete data sources, offer a viable alternative for studying hidden populations.

Purpose of the Study:

  • To present and discuss various indirect methods for estimating the size of hidden populations.
  • To outline the indications and potential biases associated with each indirect estimation technique.
  • To emphasize the importance of careful data evaluation and bias analysis for accurate population estimations.

Main Methods:

  • Review and exposition of diverse indirect estimation techniques.
  • Analysis of the applicability and limitations of each method.
  • Discussion of data sources and risk of bias in estimations.

Main Results:

  • Indirect methods provide essential tools for estimating hidden populations where direct methods fail.
  • Each method has specific indications and is associated with unique biases that must be understood.
  • Careful evaluation of available data and rigorous bias analysis are crucial for reliable estimates.

Conclusions:

  • Indirect methods are indispensable for the study of hidden populations.
  • Understanding the nuances and potential biases of each method is key to accurate epidemiological research.
  • This work complements existing literature by providing a comprehensive overview for researchers.