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

Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

10.0K
To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
10.0K
Sample Size Calculation01:19

Sample Size Calculation

7.0K
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...
7.0K
Central Limit Theorem01:14

Central Limit Theorem

22.3K
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...
22.3K
Applications of Normal Distribution01:22

Applications of Normal Distribution

10.2K
The normal distribution is a useful statistical tool. One of its practical applications is determining the door height after considering the normal distribution of heights of persons, such that many can pass through it easily without striking their heads. The normal distribution can also determine the probability of a person having a height less than a specific height.
The heights of 15 to 18-year-old males from Chile from 1984 to 1985 followed a normal distribution. The mean height is 172.36...
10.2K
Student t Distribution01:31

Student t Distribution

15.3K
The population standard deviation is rarely known in many day-to-day examples of statistics. When the sample sizes are large, it is easy to estimate the population standard deviation using a confidence interval, which provides results close enough to the original value. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
The Student t distribution was developed by William S. Goset (1876–1937) of the...
15.3K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

5.8K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
5.8K

You might also read

Related Articles

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

Sort by
Same author

MSTune: A Data-Driven Approach to Parameter Tuning Using Grid Search and Differential Evolution for Gas Chromatography-Mass Spectrometry-Based Compound Identification.

Metabolites·2026
Same author

Machine Learning-Enabled In Situ Diagnostics for Intelligent Plasma-Based Semiconductor Manufacturing: A Review.

ACS applied materials & interfaces·2026
Same author

A Novel Drug Candidate that Selectively Targets the Critical Androgen Receptor-ELK1 Growth Axis in Advanced and Drug-Resistant Prostate Cancer.

bioRxiv : the preprint server for biology·2026
Same author

Ethnic Differences in the Presentation Patterns of Type 3 Macular Neovascularization.

Retina (Philadelphia, Pa.)·2026
Same author

Designable van der Waals Crystal for Artificial Neuronal Cell Mimicking.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

IFN-γ PET imaging stratifies and predicts response to CD137 agonism in a syngeneic colorectal tumor model.

Journal for immunotherapy of cancer·2026
Same journal

Asymptotic online FWER control for dependent test statistics.

Statistical methods in medical research·2026
Same journal

Regression analysis of misclassified current status data with potentially unknown test accuracy.

Statistical methods in medical research·2026
Same journal

Bayesian multivariate linear mixed-effects models with varied association structures.

Statistical methods in medical research·2026
Same journal

Inference about the ratio of age-standardized rates between two overlapping populations.

Statistical methods in medical research·2026
Same journal

A robust neural network with random effects for subject-specific prediction of clustered count data.

Statistical methods in medical research·2026
Same journal

A comparison of methods for designing hybrid type 2 cluster-randomized trials with continuous effectiveness and implementation endpoints.

Statistical methods in medical research·2026
See all related articles

Related Experiment Video

Updated: Apr 16, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

11.1K

Sample size determination for logistic regression on a logit-normal distribution.

Seongho Kim1,2, Elisabeth Heath2, Lance Heilbrun1,2

  • 11 Biostatistics Core, Karmanos Cancer Institute, Wayne State University, Detroit, MI 48201, USA.

Statistical Methods in Medical Research
|March 7, 2015
PubMed
Summary
This summary is machine-generated.

New methods simplify sample size calculations for logistic regression. These approaches reduce the need for complex parameters and can lead to significantly smaller required sample sizes, improving efficiency in statistical studies.

Keywords:
Logistic regressionlogit-normal distributionpower calculationsample size determinationtransformation

More Related Videos

Tactile Semiautomatic Passive-Finger Angle Stimulator TSPAS
04:40

Tactile Semiautomatic Passive-Finger Angle Stimulator TSPAS

Published on: July 30, 2020

3.4K
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

Related Experiment Videos

Last Updated: Apr 16, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

11.1K
Tactile Semiautomatic Passive-Finger Angle Stimulator TSPAS
04:40

Tactile Semiautomatic Passive-Finger Angle Stimulator TSPAS

Published on: July 30, 2020

3.4K
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

Area of Science:

  • Biostatistics
  • Statistical Modeling

Background:

  • Calculating sample size for simple logistic regression is straightforward.
  • Multiple logistic regression sample size determination often requires unavailable data, like the coefficient of determination ([Formula: see text]).

Purpose of the Study:

  • To propose novel methods for sample size calculation in simple and multiple logistic regression.
  • To address limitations of existing methods by leveraging the logit-normal distribution property of logistic regression.

Main Methods:

  • Utilizing the property that logistic regression's response variable follows a logit-normal distribution.
  • Applying a normal transformation to outcome measures for sample size determination.
  • Developing methods applicable to simple and multiple logistic regression scenarios.

Main Results:

  • Proposed methods eliminate the need for the coefficient of determination ([Formula: see text]) in multiple logistic regression.
  • The new methods support interim or group-sequential study designs.
  • Simulation studies indicate a significantly smaller required sample size compared to existing methods.

Conclusions:

  • The developed methods offer a more practical and efficient approach to sample size calculation for logistic regression.
  • These advancements facilitate more streamlined and potentially less resource-intensive statistical studies.