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

3.3K
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...
3.3K
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

125
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
125
Contaminants and Errors01:16

Contaminants and Errors

88
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...
88
Margin of Error01:27

Margin of Error

4.0K
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.
4.0K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

176
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
176
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

349
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
349

You might also read

Related Articles

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

Sort by
Same author

Plasma Glial Fibrillary Acidic Protein (GFAP) shows age-dependent associations with externalizing psychopathology and atypical brain connectivity.

Translational psychiatry·2026
Same author

Prevalence of Common Mental Health Problems Among Transgender and Non-Binary Individuals During the COVID-19 Pandemic: A Systematic Review & Meta-Analysis.

The International journal of social psychiatry·2026
Same author

Prevalence of health issues among youth in Kolar - findings from a large-scale population-based cross-sectional study.

Journal of family medicine and primary care·2026
Same author

Intraclass correlation in cluster randomized controlled trials for anxiety disorders: Evidence from a meta-analysis.

Indian journal of psychiatry·2026
Same author

Performance evaluation of dimensionality reduction techniques on high-dimensional DNA methylation data.

The international journal of biostatistics·2026
Same author

Authors' response.

The Indian journal of medical research·2026

Related Experiment Video

Updated: Jun 22, 2025

A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition
00:04

A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition

Published on: September 20, 2019

10.6K

Simplified Sample Size Formulas for Detecting a Medically Important Effect.

Abhaya Indrayan1, Aman Mishra1, Binukumar Bhaskarapillai2

  • 1Department of Clinical Research, Max Healthcare, Saket, Delhi, India.

Indian Journal of Community Medicine : Official Publication of Indian Association of Preventive & Social Medicine
|June 27, 2024
PubMed
Summary
This summary is machine-generated.

Determining the correct sample size is crucial for reliable medical research. This guide provides simple, unified formulas for hypothesis testing sample sizes, aiding researchers in selecting appropriate methods.

Keywords:
Detecting an effectmedical significancesample sizesimplified formulastest of hypothesis

More Related Videos

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

490
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K

Related Experiment Videos

Last Updated: Jun 22, 2025

A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition
00:04

A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition

Published on: September 20, 2019

10.6K
Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

490
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K

Area of Science:

  • Biostatistics
  • Medical Research Methodology

Background:

  • Sample size determination is a common challenge for medical researchers.
  • Inappropriate sample sizes can lead to missed important medical effects.
  • Numerous complex formulas exist, causing confusion among researchers and statisticians.

Purpose of the Study:

  • To provide a unified collection of simple, correct sample size formulas.
  • To present formulas applicable to various research settings in one place.
  • To aid researchers in confidently selecting and applying appropriate sample size calculations for hypothesis testing.

Main Methods:

  • Compilation of existing sample size formulas for hypothesis testing.
  • Simplification and uniform presentation of these formulas.
  • Inclusion of applicable research settings for each formula.

Main Results:

  • A consolidated resource of straightforward sample size formulas.
  • Guidance on selecting the correct formula based on research context.
  • Addresses discrepancies found in existing literature and software.

Conclusions:

  • This resource simplifies sample size calculation for medical researchers.
  • Facilitates accurate sample size determination, enhancing study reliability.
  • A unique, comprehensive collection not previously available in a single source.