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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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Accuracy and Errors in Hypothesis Testing01:13

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Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
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Contaminants and Errors01:16

Contaminants and Errors

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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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Testing a Claim about Standard Deviation01:19

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A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
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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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Margin of Error01:27

Margin of Error

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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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Related Experiment Video

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Assessment of Child Anthropometry in a Large Epidemiologic Study
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Sample size calculation for data reliability and diagnostic performance: a go-to review.

Caterina Beatrice Monti1, Federico Ambrogi2,3, Francesco Sardanelli3,4

  • 1Postgraduation School in Radiodiagnostics, University of Milan, Milan, Italy. caterinab.monti@gmail.com.

European Radiology Experimental
|July 4, 2024
PubMed
Summary
This summary is machine-generated.

Calculating the correct sample size is crucial for study precision and statistical power. This review provides accessible methods for sample size calculations in reliability and diagnostic performance studies.

Keywords:
Data scienceROC curveReproducibility of resultsSample sizeSensitivity and specificity

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Area of Science:

  • Biostatistics
  • Medical Research Methodology

Background:

  • Sample size determination is essential for study validity and ethical considerations.
  • Inadequate sample sizes can lead to underpowered studies, wasting resources and potentially harming participants.

Purpose of the Study:

  • To provide a review of sample size calculation methods for studies on data reliability and diagnostic performance.
  • To offer practical guidance and accessible methods for researchers in these fields.

Main Methods:

  • Review of established statistical methods for sample size calculation.
  • Consideration of reliability metrics (Cohen's κ, ICC, Bland-Altman) and diagnostic performance metrics (accuracy, sensitivity, specificity, ROC curves).
  • Discussion of special cases including dropouts, multiple endpoints, and non-standard error thresholds.

Main Results:

  • Methods for calculating sample sizes for repeatability/reproducibility and diagnostic accuracy are presented.
  • Guidance is provided for various scenarios, including comparisons and estimations.
  • Examples of freely available software for common calculations are included.

Conclusions:

  • Appropriate sample size calculation is fundamental for ensuring the quality and reliability of research findings.
  • This review serves as a practical resource for researchers needing to determine sample sizes for specific study types.