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Related Concept Videos

Sample Size Calculation01:19

Sample Size Calculation

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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...
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Sample Proportion and Population Proportion01:20

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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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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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Testing a Claim about Population Proportion01:24

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A complete procedure for testing a claim about a population proportion is provided here.
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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 +...
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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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Sample size calculation for prevalence studies using Scalex and ScalaR calculators.

Lin Naing1, Rusli Bin Nordin2, Hanif Abdul Rahman3,4,5

  • 1PAPRSB Institute of Health Sciences, Universiti Brunei Darussalam, Jalan Tungku Link, Brunei-Muara BE3119, Gadong, Brunei Darussalam. ayub.sadiq@ubd.edu.bn.

BMC Medical Research Methodology
|July 30, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces user-friendly calculators for determining sample size in prevalence studies, simplifying the process for researchers. These tools aid in selecting appropriate parameters and reporting, minimizing calculation errors.

Keywords:
CalculatorPrevalence studiesSample sizeSingle proportion

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Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Existing literature provides guidance on sample size calculation for prevalence studies.
  • However, practical application and reporting can be challenging for researchers.

Purpose of the Study:

  • To guide, assist, and report sample size calculation for prevalence studies.
  • To simplify the informed decision-making process for researchers.
  • To minimize errors in parameter selection, calculation, and reporting.

Main Methods:

  • Discussion of four key parameters: level of confidence, precision, data variability, and anticipated loss.
  • Demonstration of purposely-designed calculators for sample size determination.
  • Provision of calculators compatible with free software (Spreadsheet and RStudio).

Main Results:

  • Calculators facilitate informed decision-making for sample size calculation.
  • The tools assist in preparing appropriate reports for prevalence studies.
  • Proper understanding and selection of parameters are emphasized.

Conclusions:

  • The developed calculators aim to minimize errors in sample size calculation and reporting.
  • Free software compatibility benefits researchers with limited resources.
  • Accessible online resources are provided to support researchers.