Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

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...
6.9K
Contaminants and Errors01:16

Contaminants and Errors

520
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...
520
What are Estimates?01:06

What are Estimates?

9.1K
It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
9.1K
Sample Proportion and Population Proportion01:20

Sample Proportion and Population Proportion

7.0K
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...
7.0K
Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

8.6K
On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
8.6K
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.5K
When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
3.5K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

European Society for Paediatric Endocrinology and European Society of Endocrinology Joint Clinical Practice Guidance for Healthcare Transition from Paediatric to Adult Endocrine Care.

Hormone research in paediatrics·2026
Same author

European Society for Paediatric Endocrinology (ESPE) and European Society of Endocrinology (ESE) joint clinical practice guidance for healthcare transition from paediatric to adult endocrine care.

European journal of endocrinology·2026
Same author

How Often do Youth ask Their Providers the Questions They Checked on an Attention-Deficit Hyperactivity Disorder Question Prompt List?

Journal of developmental and behavioral pediatrics : JDBP·2026
Same author

Fifty Years of Children's Nursing: Reflections on Practice, Research and Future Directions.

Journal of advanced nursing·2026
Same author

Healthcare transition for adolescents with chronic conditions: time for change.

Nature reviews. Disease primers·2025
Same author

A Tutorial on Conducting and Interpreting a Bayesian Independent T-Test Using Open-Source Software.

Journal of advanced nursing·2025

Related Experiment Video

Updated: Mar 21, 2026

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems
07:41

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems

Published on: July 30, 2019

8.1K

Fundamentals of estimating sample size.

Helen Evelyn Malone1, Honor Nicholl1, Imelda Coyne2

  • 1School of Nursing & Midwifery Trinity College Dublin, Republic of Ireland.

Nurse Researcher
|May 19, 2016
PubMed
Summary
This summary is machine-generated.

Understanding four key parameters—significance level, power, effect size, and data variability—is crucial for accurate sample size estimation in quantitative research. This knowledge simplifies calculating sample sizes for common nursing study designs.

Keywords:
effect sizepowerquantitative researchsample sizesignificance levelstandard deviation

More Related Videos

Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

36.1K
Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

4.5K

Related Experiment Videos

Last Updated: Mar 21, 2026

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems
07:41

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems

Published on: July 30, 2019

8.1K
Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

36.1K
Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

4.5K

Area of Science:

  • Quantitative research methodology
  • Biostatistics
  • Nursing research

Background:

  • Accurate sample size estimation is essential for planning quantitative studies.
  • Existing sample size calculation literature can be complex and in-depth.

Purpose of the Study:

  • To provide an overview of the four fundamental parameters for sample size determination.
  • To explain sample size estimation for three common nursing study designs.

Main Methods:

  • Explanation of four core parameters: significance level, power, effect size, and data variability (standard deviation or event rate).
  • Application of these parameters to calculate sample sizes for three study designs: comparison of two independent means, paired mean design, and comparison of two proportions.

Main Results:

  • Comprehension of the four parameters enables researchers to estimate basic sample sizes.
  • Informed parameter selection ensures adequate statistical power to detect meaningful effects.

Conclusions:

  • Understanding sample size parameters is foundational for complex calculations and software use.
  • Statistically sound findings, crucial for evidence-based practice, rely on informed sample size estimations and clear reporting of parameter rationales.