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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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Contaminants and Errors01:16

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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.
Another key consideration is determining the appropriate number of samples required to...
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One-Way ANOVA: Unequal Sample Sizes01:15

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One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
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One-Way ANOVA: Equal Sample Sizes01:15

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One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
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In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
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Sampling Methods: Overview01:06

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A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
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Methods of Soil Resampling to Monitor Changes in the Chemical Concentrations of Forest Soils
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Incurred sample reanalysis: adjusted procedure for sample size calculation.

Piotr J Rudzki1, Katarzyna Buś-Kwaśnik1, Michał Kaza1

  • 1Pharmacology Department, Pharmaceutical Research Institute, 8 Rydygiera Street, Warsaw 01-793, Poland.

Bioanalysis
|November 10, 2017
PubMed
Summary
This summary is machine-generated.

Optimized incurred sample reanalysis (ISR) calculations reduce unnecessary testing by up to 66%, ensuring bioanalytical reliability without impacting study outcomes. This streamlined approach aligns with regulatory standards for drug development.

Keywords:
ISRbioanalytical method validationincurred sample reanalysispharmacokinetics

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Area of Science:

  • Bioanalysis
  • Pharmaceutical Development
  • Regulatory Science

Background:

  • Incurred sample reanalysis (ISR) is crucial for validating bioanalytical data reliability.
  • Current regulatory guidelines recommend reanalyzing up to 10% of study samples for ISR.
  • Not all reanalyzed samples are essential for evaluating the overall test result.

Purpose of the Study:

  • To optimize the calculation of ISR sample size.
  • To eliminate negligible reanalyses, thereby reducing unnecessary testing.
  • To ensure bioanalytical reliability while improving efficiency.

Main Methods:

  • Developed an optimized procedure for calculating ISR sample size.
  • Implemented criteria to identify and eliminate negligible reanalyses.
  • Validated the adjusted procedure against existing methods.

Main Results:

  • The optimized procedure effectively eliminates negligible ISRs, reducing the number of reanalyses by up to 66%.
  • This reduction in reanalysis does not compromise the accuracy or reliability of the bioanalytical test outcome.
  • The adjusted procedure is universally applicable to studies compliant with EMA and US FDA requirements.

Conclusions:

  • The optimized ISR sample size calculation enhances the efficiency of bioanalytical method validation.
  • This approach addresses the potential mismatch between current ISR recommendations and acceptance criteria.
  • The procedure supports the evolution of bioanalytical standards for both small and large molecules.