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Related Concept Videos

Sample Size Calculation01:19

Sample Size Calculation

Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
Contaminants and Errors01:16

Contaminants and Errors

Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...
Sampling Distribution01:12

Sampling Distribution

Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
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...
Distributions to Estimate Population Parameter01:26

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...
Margin of Error01:27

Margin of Error

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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Related Experiment Video

Updated: Jun 14, 2026

Setting Limits on Supersymmetry Using Simplified Models
07:46

Setting Limits on Supersymmetry Using Simplified Models

Published on: November 15, 2013

Simulation sample sizes for Monte Carlo partial EVPI calculations.

Jeremy E Oakley1, Alan Brennan, Paul Tappenden

  • 1Department of Probability and Statistics, University of Sheffield, The Hicks Building, Hounsfield Road, Sheffield, S3 7RH, UK. j.oakley@sheffield.ac.uk <j.oakley@sheffield.ac.uk>

Journal of Health Economics
|April 10, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a new algorithm to accurately estimate the bias and confidence interval width for partial expected value of perfect information (EVPI) calculations using Monte Carlo methods. The algorithm provides a reliable way to determine the necessary number of samples for accurate EVPI estimates.

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Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

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Area of Science:

  • Decision Analysis
  • Computational Statistics
  • Health Economics

Background:

  • Partial expected value of perfect information (EVPI) assesses the value of reducing uncertainty in decision models.
  • Current Monte Carlo methods for EVPI can be computationally intensive and prone to bias with insufficient inner samples.

Purpose of the Study:

  • To develop a simple, quick algorithm for estimating EVPI bias and confidence interval width.
  • To guide the determination of optimal inner and outer sample sizes for accurate EVPI estimation.

Main Methods:

  • A novel algorithm is presented to estimate EVPI bias and confidence interval width.
  • The method involves a nested Monte Carlo approach with a focus on sample size optimization.
  • The algorithm's performance is validated through three distinct case studies.

Main Results:

  • The proposed algorithm provides accurate estimates of EVPI bias and confidence interval width.
  • It requires a relatively small number of model runs (approximately 600), making it computationally efficient.
  • The algorithm effectively determines the required number of outer and inner iterations for desired accuracy.

Conclusions:

  • This algorithm offers a practical solution for improving the accuracy and efficiency of partial EVPI calculations.
  • It aids researchers in optimizing computational resources for decision modeling.
  • The findings are applicable to various fields employing decision analysis and uncertainty quantification.