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

6.8K
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...
6.8K
Margin of Error01:27

Margin of Error

7.8K
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.
7.8K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.9K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
8.9K
Testing a Claim about Mean: Known Population SD01:11

Testing a Claim about Mean: Known Population SD

3.3K
A complete procedure of testing the hypothesis about a population mean is explained here.
Estimating a population mean requires the samples to be distributed normally. The data should be collected from the randomly selected samples having no sampling bias. The sample size needed to be higher than 30, and most importantly, the population standard deviation should be already known.
In most realistic situations, the population standard deviation is often unknown, but in rare circumstances, when it...
3.3K
Testing a Claim about Mean: Unknown Population SD01:21

Testing a Claim about Mean: Unknown Population SD

6.3K
A complete procedure of testing a hypothesis about a population mean when the population standard deviation is unknown is explained here.
Estimating a population mean requires the samples to be approximately normally distributed. The data should be collected from the randomly selected samples having no sampling bias. There is no specific requirement for sample size. But if the sample size is less than 30, and we don't know the population standard deviation, a different approach is used;...
6.3K
Censoring Survival Data01:09

Censoring Survival Data

596
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
596

You might also read

Related Articles

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

Sort by
Same author

Prior exposure to advanced therapy and timing of discontinuation and risk of serious infections in patients with inflammatory bowel disease initiating a new advanced therapy.

Journal of Crohn's & colitis·2026
Same author

Adjunctive GLP1 Receptor Agonists in Patients With Inflammatory Bowel Diseases and Obesity and/or Diabetes: A Target Trial Emulation.

Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association·2026
Same author

Safety of advanced therapies during pregnancy in women with immune-mediated inflammatory diseases: a systematic review and meta-analysis.

Journal of Crohn's & colitis·2026
Same author

The Relationship Between Maternal Nonnarcotic Analgesics Allergy Labels and Maternal and Fetal Outcomes: Results From a Large Administrative Cohort.

The journal of allergy and clinical immunology. In practice·2026
Same author

Treatment and Outcomes of Crohn's Disease and Ulcerative Colitis in Newly Diagnosed Adults in the United States, 2007 to 2023.

Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association·2026
Same author

Antibiotic Exposure Through Human Milk Influences the Infant Gut Microbiome.

bioRxiv : the preprint server for biology·2026

Related Experiment Video

Updated: Feb 17, 2026

Double In Utero Electroporation to Target Temporally and Spatially Separated Cell Populations
10:45

Double In Utero Electroporation to Target Temporally and Spatially Separated Cell Populations

Published on: June 14, 2020

8.0K

A sample size calculation for spontaneous abortion in observational studies.

Ronghui Xu1, Christina Chambers

  • 1Department of Family and Preventive Medicine, University of California-San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA. rxu@ucsd.edu

Reproductive Toxicology (Elmsford, N.Y.)
|September 13, 2011
PubMed
Summary

Calculating sample size for spontaneous abortion (SAB) studies is complex due to left truncation and loss to follow-up. This study presents a simplified method for sample size calculation in observational pregnancy studies, requiring minimal assumptions.

More Related Videos

Anogenital Distance and Perineal Measurements of the Pelvic Organ Prolapse POP Quantification System
03:49

Anogenital Distance and Perineal Measurements of the Pelvic Organ Prolapse POP Quantification System

Published on: September 20, 2018

21.1K
The 4-vessel Sampling Approach to Integrative Studies of Human Placental Physiology In Vivo
12:17

The 4-vessel Sampling Approach to Integrative Studies of Human Placental Physiology In Vivo

Published on: August 2, 2017

11.3K

Related Experiment Videos

Last Updated: Feb 17, 2026

Double In Utero Electroporation to Target Temporally and Spatially Separated Cell Populations
10:45

Double In Utero Electroporation to Target Temporally and Spatially Separated Cell Populations

Published on: June 14, 2020

8.0K
Anogenital Distance and Perineal Measurements of the Pelvic Organ Prolapse POP Quantification System
03:49

Anogenital Distance and Perineal Measurements of the Pelvic Organ Prolapse POP Quantification System

Published on: September 20, 2018

21.1K
The 4-vessel Sampling Approach to Integrative Studies of Human Placental Physiology In Vivo
12:17

The 4-vessel Sampling Approach to Integrative Studies of Human Placental Physiology In Vivo

Published on: August 2, 2017

11.3K

Area of Science:

  • Obstetrics and Gynecology
  • Epidemiology
  • Biostatistics

Background:

  • Spontaneous abortion (SAB) is a critical outcome in pregnancy studies for drugs and vaccines.
  • Observational studies, like prospective cohorts or pregnancy registries, often evaluate SAB risk.
  • Participants are frequently enrolled post-pregnancy recognition, leading to left-truncated data.

Purpose of the Study:

  • To develop a simplified method for calculating sample size in spontaneous abortion studies.
  • To address the complexities of left truncation and loss to follow-up in SAB data analysis.
  • To provide a practical approach requiring minimal assumptions about population and observed SAB rates.

Main Methods:

  • Utilizing survival analysis methods to account for left truncation in SAB data.
  • Developing a straightforward sample size calculation approach.
  • Applying the method to a hypothetical prospective study evaluating SAB risk.

Main Results:

  • A simplified sample size calculation method for SAB studies is proposed.
  • The method requires minimal assumptions regarding population and observed SAB rates.
  • Demonstrates applicability through a hypothetical prospective study example.

Conclusions:

  • A simplified sample size calculation is feasible for spontaneous abortion studies.
  • This method can aid researchers in designing observational pregnancy outcome studies.
  • Addresses key statistical challenges including left truncation and loss to follow-up.