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Related Concept Videos

Sample Size Calculation01:19

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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.
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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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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.
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A complete procedure for testing a claim about a population proportion is provided here.
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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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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.
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The sample size matters: evaluating minimum and reasonable values in prevalence studies.

Volodimir Sarabeev1, Svitlana Shvydka2, Olga Lisitsyna3

  • 1Institute of Parasitology, Slovak Academy of Sciences, Hlinkova 3, 04001 Košice, Slovak Republic; Department of Biology, Zaporizhzhia National University, Universytetska 66, 69011 Zaporizhzhia, Ukraine.

International Journal for Parasitology
|June 1, 2025
PubMed
Summary
This summary is machine-generated.

Determining adequate sample size is crucial for accurate prevalence studies. A minimum of 16-45 individuals is suggested for 10-90% prevalence, but aim for 110-135 for reduced uncertainty.

Keywords:
Bag of Little BootstrapsBootstrap medianConfidence intervalNon-parametric bootstrapPrecisionPrevalenceSample size determination

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Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Accurate sample size estimation is vital for the validity of prevalence studies.
  • Prevalence estimates are influenced by sample size, affecting research outcomes.

Purpose of the Study:

  • To identify constraints in prevalence assessment.
  • To provide guidance on minimum and reasonable sample size determination.
  • To analyze prevalence properties in relation to sample size.

Main Methods:

  • Constraint analysis of sample size and precision.
  • Visualization of median prevalence, confidence intervals, and precision changes.
  • Assessment of prevalence properties as a function of sample size.

Main Results:

  • Sample sizes below 15 individuals often yield unacceptable precision.
  • Minimum sample size can range from 16 to over 450 individuals (1%-99% prevalence).
  • A practical minimum: sample until 5 cases and 5 non-cases are found (except for extreme prevalences).
  • Sample sizes of 110-135 individuals show diminishing returns in reducing uncertainty for 5%-95% prevalence.

Conclusions:

  • Sample size selection should balance study objectives, resources, and desired precision.
  • Authors, editors, and reviewers should consider sample size alongside actual prevalence.
  • Acknowledge limitations if minimum sample size is not met, as all data contribute to understanding pathogen distribution.