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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...
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

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.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Sampling Methods: Overview01:06

Sampling Methods: Overview

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. 
In analytical chemistry, the choice of sampling...
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

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.
As a first step, the hypothesis (null and alternative) concerning the claim about...

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Updated: Jun 17, 2026

Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

Sample size calculations: basic principles and common pitfalls.

Marlies Noordzij1, Giovanni Tripepi, Friedo W Dekker

  • 1ERA-EDTA Registry, Department of Medical Informatics, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands. m.noordzij@amc.uva.nl

Nephrology, Dialysis, Transplantation : Official Publication of the European Dialysis and Transplant Association - European Renal Association
|January 14, 2010
PubMed
Summary

Determining the correct sample size is crucial for study design. This paper clarifies sample size calculation principles, common errors, and reporting methods for investigators.

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Last Updated: Jun 17, 2026

Sampling Soils in a Heterogeneous Research Plot
07:11

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Published on: January 7, 2019

Measuring Delay Discounting in Humans Using an Adjusting Amount Task
07:47

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Published on: January 9, 2016

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Research Methodology

Background:

  • Sample size calculation is a frequent request from researchers.
  • Accurate sample size is fundamental for study validity and statistical power.
  • Existing statistical texts offer methods, but choosing the right one can be challenging for investigators.

Purpose of the Study:

  • To elucidate fundamental principles of sample size determination.
  • To highlight common pitfalls encountered during sample size calculations.
  • To provide guidance on the appropriate reporting of sample size justifications.

Main Methods:

  • Discussion of core statistical concepts related to sample size.
  • Review of various formulas applicable to different data types and study designs.
  • Emphasis on the sensitivity of sample size calculations to input parameters.

Main Results:

  • Sample size calculations are highly sensitive to input parameters, requiring careful selection.
  • Numerous formulas exist, but their application necessitates understanding of underlying assumptions.
  • Potential for significant discrepancies in required sample size due to minor parameter variations.

Conclusions:

  • Investigators must exercise caution when applying sample size formulas due to their sensitivity.
  • Understanding basic principles and common pitfalls is essential for accurate sample size justification.
  • Standardized reporting of sample size calculations enhances study transparency and reproducibility.