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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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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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Central Limit Theorem01:14

Central Limit Theorem

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The central limit theorem, abbreviated as clt, is one of the most powerful and useful ideas in all of statistics. The central limit theorem for sample means says that if you repeatedly draw samples of a given size and calculate their means, and create a histogram of those means, then the resulting histogram will tend to have an approximate normal bell shape. In other words, as sample sizes increase, the distribution of means follows the normal distribution more closely.
The sample size, n, that...
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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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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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Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

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A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
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Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
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The power of sample size calculations.

Marieke S Jansen1, Rolf H H Groenwold1,2, Olaf M Dekkers1,3,4

  • 1Department of Clinical Epidemiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands.

European Journal of Endocrinology
|October 29, 2024
PubMed
Summary

This paper simplifies sample size calculations, a common challenge in research. Understanding sample size estimation is crucial for designing robust scientific studies and interpreting results accurately.

Keywords:
clinical trialsepidemiologyobservational researchpower analysissample size calculation

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Area of Science:

  • Biostatistics
  • Research Methodology
  • Scientific Writing

Background:

  • Sample size calculations are ubiquitous in scientific literature and research projects.
  • Despite their prevalence, sample size estimation is often misunderstood and perceived as difficult.
  • A clear understanding of sample size is fundamental for research integrity.

Purpose of the Study:

  • To provide a foundational overview of sample size estimation.
  • To demystify the process of calculating sample sizes for researchers and reviewers.
  • To enhance the comprehension and application of sample size principles in scientific work.

Main Methods:

  • This paper offers a conceptual overview, not a specific statistical method.
  • It focuses on the fundamental principles underlying sample size determination.
  • The content is designed for broad accessibility to researchers across disciplines.

Main Results:

  • The paper clarifies the core concepts of sample size estimation.
  • It aims to reduce the perceived difficulty associated with sample size calculations.
  • Readers will gain a better understanding of why and how sample sizes are determined.

Conclusions:

  • Effective sample size estimation is essential for valid scientific research.
  • This overview serves as a guide to the fundamentals of sample size calculation.
  • Improved understanding can lead to more rigorous study designs and reliable findings.