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

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

718
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
718
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.5K
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...
4.5K
Data: Types and Distribution01:19

Data: Types and Distribution

2.2K
In biostatistics, data are the observations collected for analysis. There are two main types: parametric and non-parametric. Parametric data, which include continuous (e.g., weight) and discrete numerical data (e.g., number of tablets), assume a particular distribution pattern, often the normal distribution. Non-parametric data do not adhere to a specific distribution and typically comprise nominal (e.g., gender) and ordinal categorical data (e.g., pain scale ratings).
Distributions in...
2.2K
Sampling Distribution01:12

Sampling Distribution

17.6K
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...
17.6K
Probability Distributions01:32

Probability Distributions

9.9K
 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
9.9K
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

14.4K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
14.4K

You might also read

Related Articles

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

Sort by
Same author

Integrating theory and machine learning to reveal determinants of plasmid copy number.

Nature communications·2026
Same author

Comparative efficacy of novel biomaterials versus traditional materials in the treatment of dental and pulpal diseases: a systematic review and meta-analysis.

Frontiers in medicine·2026
Same author

Mapping single-cell responses to population-level dynamics during antibiotic treatment.

Molecular systems biology·2026
Same author

Classification accuracy of a hierarchical molecular inference-based deep-learning system for CNS tumour diagnosis: a multi-institutional, retrospective study.

The Lancet. Oncology·2026
Same author

A foundation model for microbial growth dynamics.

bioRxiv : the preprint server for biology·2026
Same author

Spatial proximity dictates bacterial competition and expansion in microbial communities.

Nature communications·2025
Same journal

Anisotropic unbinding and location-dependent hovering of a kinesin motor head over microtubule.

Biophysical journal·2026
Same journal

Kinesin-5/Cut7 C-terminal tail phosphorylation influence on motor regulation through multi-scale molecular modeling.

Biophysical journal·2026
Same journal

Dynamic conformations of fluorophores on self-labeling protein tags.

Biophysical journal·2026
Same journal

Different actions of RyR2 open and closed channel block explained by a multiscale Ca<sup>2+</sup> release model.

Biophysical journal·2026
Same journal

Membrane Environment Sets the Functional pK<sub>a</sub> of Ionizable Lipids.

Biophysical journal·2026
Same journal

Distinguishable spreading dynamics in microbial communities.

Biophysical journal·2026
See all related articles

Related Experiment Video

Updated: Apr 24, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.4K

Stochastic sensitivity analysis and kernel inference via distributional data.

Bochong Li1, Lingchong You2

  • 1Department of Biomedical Engineering, Duke University, Durham, North Carolina.

Biophysical Journal
|September 5, 2014
PubMed
Summary
This summary is machine-generated.

Cellular noise is inherent, but molecule distribution across cell populations is predictable. This study introduces a computational framework and regression model to analyze these distributions for inferring biological network states and enabling synthetic biology applications.

More Related Videos

Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

5.6K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.1K

Related Experiment Videos

Last Updated: Apr 24, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.4K
Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

5.6K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.1K

Area of Science:

  • Systems Biology
  • Computational Biology
  • Biophysics

Background:

  • Cellular processes exhibit inherent stochasticity, making single-cell molecule quantification unpredictable.
  • However, molecule distributions across cell populations often reveal deterministic patterns governed by regulatory networks.

Purpose of the Study:

  • To develop a computational framework for analyzing distributional data in biological systems.
  • To infer regulatory network states from population-level molecular distributions.
  • To enable robust biocomputation in synthetic biology.

Main Methods:

  • Developed a computational framework to analyze the sensitivity of distributional output to external stimuli.
  • Established a probability-divergence-based kernel regression model for inferring signal levels from distribution measurements.

Main Results:

  • The framework efficiently characterizes distributional output sensitivity.
  • The kernel regression model accurately infers signal levels from distribution data.
  • Methodology is applicable to diverse biological systems with stochastic dynamics.

Conclusions:

  • Population-level information processing is crucial for organism-level functionality.
  • The developed methods lay the foundation for engineering synthetic biological systems for biocomputation tasks like diagnostics and sensing.