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Related Concept Videos

Sample Size Calculation01:19

Sample Size Calculation

6.9K
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

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.
Another key consideration is determining the appropriate number of samples required to...
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Sample Proportion and Population Proportion01:20

Sample Proportion and Population Proportion

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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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Systematic Sampling Method01:17

Systematic Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
Systematic sampling is one of the simplest methods...
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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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Sampling Distribution01:12

Sampling Distribution

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Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
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Related Experiment Video

Updated: Mar 13, 2026

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems
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Sample size calculation: Basic principles.

Sabyasachi Das1, Koel Mitra1, Mohanchandra Mandal2

  • 1Department of Anaesthesiology and Critical Care, Medical College, Kolkata, West Bengal, India.

Indian Journal of Anaesthesia
|October 13, 2016
PubMed
Summary
This summary is machine-generated.

Calculating the correct sample size is crucial for reliable clinical research. Proper sample size estimation ensures study validity and efficient resource use.

Keywords:
Effect sizemedical researchpowerrandomized control trialresearch hypothesissample sizestudy design

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Area of Science:

  • Clinical Research Methodology
  • Biostatistics

Background:

  • Determining appropriate sample size is a critical planning step in clinical studies.
  • Inadequate sample sizes risk invalid or imprecise results, while excessive sizes are resource-intensive and potentially unethical.

Approach:

  • Emphasizes the importance of a priori sample size calculation before study commencement.
  • Discourages conventional post hoc sample size computation.

Key Points:

  • Adequate sample size minimizes random error and enhances statistical power.
  • Ensures a balance between detecting true differences and achieving reasonable precision.
  • Transparency in sample size calculation is essential for justification and replication.

Conclusions:

  • A priori sample size estimation is fundamental for robust and ethical clinical research.
  • Properly calculated sample sizes improve study validity, efficiency, and reproducibility.