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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.
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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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Group Design02:01

Group Design

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The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
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Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
An example of the experimental method is a public...
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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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Updated: Mar 18, 2026

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How to Calculate Sample Size for an Experiment: A Case-Based Description.

R Mera1, H Thompson2, C Prasad3

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Summary
This summary is machine-generated.

This study simplifies experimental design and statistical analysis for researchers. It focuses on calculating the appropriate sample size for single factor and repeated measures experiments using real data.

Keywords:
ExperimentsPowerRepeated measuresSample sizeTreatment effect

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Area of Science:

  • Experimental design
  • Statistical analysis
  • Biostatistics

Background:

  • Accurate sample size is crucial for reliable experimental results.
  • Many researchers struggle with appropriate sample size determination.
  • This series aims to simplify complex statistical concepts.

Purpose of the Study:

  • To provide a simplified guide to sample size calculation.
  • To illustrate methods using real laboratory data.
  • To cover single factor and repeated measures designs.

Main Methods:

  • Focus on practical sample size calculation techniques.
  • Utilizes actual experimental data for examples.
  • Explains methods for single factor experiments.
  • Explains methods for repeated measures designs.

Main Results:

  • Demonstrates sample size calculation for common experimental designs.
  • Provides a framework for understanding statistical power and effect size.
  • Offers practical examples applicable to laboratory settings.

Conclusions:

  • Proper sample size calculation enhances the validity of research findings.
  • Simplified statistical approaches can improve research practices.
  • This article serves as a foundational guide for subsequent statistical topics.