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Related Concept Videos

Sample Proportion and Population Proportion01:20

Sample Proportion and Population Proportion

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Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...
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An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
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The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
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A complete procedure for testing a claim about a population proportion is provided here.
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It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
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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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Estimation and Inference for the Mediation Proportion.

Daniel Nevo1, Xiaomei Liao1, Donna Spiegelman1

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The International Journal of Biostatistics
|September 21, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical method for mediation analysis, crucial for understanding how risk factors influence health outcomes through other variables. The developed approach provides reliable estimation and inference for the mediation proportion, enhancing epidemiological research.

Keywords:
mediation analysismediation proportionproportion of treatment effectthe difference method

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Area of Science:

  • Epidemiology
  • Public Health
  • Social Science
  • Biostatistics

Background:

  • Mediation analysis is vital in epidemiology and social sciences to understand indirect effects of risk factors on outcomes.
  • The mediation proportion is a key metric, but rigorous estimation and inference methods have been lacking.
  • Existing 'difference methods' for estimating mediation proportion lack robust statistical foundations.

Purpose of the Study:

  • To develop a rigorous statistical methodology for estimating and performing inference on the mediation proportion.
  • To formulate this methodology within the framework of Cox and generalized linear models.
  • To address the assumption of 'g-linkability' between marginal and conditional models.

Main Methods:

  • Utilized a data duplication algorithm combined with generalized estimation equations (GEE) for robust estimation.
  • Developed methods applicable to Cox and generalized linear models.
  • Investigated the 'g-linkability' assumption, where marginal and conditional models share the same link function.

Main Results:

  • The proposed methodology provides valid estimation and inference for the mediation proportion when g-linkability holds.
  • Extensive simulation studies confirmed the finite sample properties of the new approach.
  • The methodology was successfully applied to analyze breast cancer incidence in the Nurses' Health Study.

Conclusions:

  • The developed statistical framework offers a rigorous solution for mediation proportion estimation and inference in epidemiological studies.
  • The approach is flexible, applicable to various regression models, and supported by available software (SAS, R).
  • This work advances the statistical toolkit for dissecting complex causal pathways in health research.