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 Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

1.1K
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:
1.1K
Random Error01:04

Random Error

10.0K
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
10.0K
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

16.8K
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...
16.8K
Statistical Significance01:50

Statistical Significance

23.1K
Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
23.1K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

7.2K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
7.2K
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

656
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
656

You might also read

Related Articles

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

Sort by
Same author

Deep learning with limited data: a transfer learning approach for transcriptomic survival prediction.

Computers in biology and medicine·2026
Same author

[What factors contributed to the higher incidence rate of in-hospital falls at the time of Covid 19? A paradigm shift?]

Igiene e sanita pubblica·2022
Same author

Correction to: New clotting disorders that cast new light on blood coagulation and may play a role in clinical practice.

Journal of thrombosis and thrombolysis·2018
Same author

The epistemic and aleatory uncertainties of the ETAS-type models: an application to the Central Italy seismicity.

Scientific reports·2017
Same author

New clotting disorders that cast new light on blood coagulation and may play a role in clinical practice.

Journal of thrombosis and thrombolysis·2017
Same author

Prethrombotic, prothrombotic, thrombophilic states, hypercoagulable state, thrombophilia etc.: semantics should be respected even in medical papers.

Journal of thrombosis and thrombolysis·2016
Same journal

Application of ephrin-B2 loaded glycol chitosan-silk fibroin hydrogel in the treatment of diabetic refractory wounds.

Scientific reports·2026
Same journal

International expert Delphi consensus on thromboprophylaxis in metabolic and bariatric surgery.

Scientific reports·2026
Same journal

Assessing the cross-region knowledge transfer capability of selected deep learning building vectorization methods in the context of available training datasets.

Scientific reports·2026
Same journal

Feasibility and preliminary effects of outdoor versus indoor cognitive-motor therapy in women with Alzheimer's disease: A randomized single-blind pilot study.

Scientific reports·2026
Same journal

Hallmarks of social action in the vocal turn-taking of wild common marmosets (Callithrix jacchus).

Scientific reports·2026
Same journal

Role and mechanism of AOPPs-induced NOX4-mediated ferroptosis in intervertebral disc degeneration.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: Mar 6, 2026

Kinematic History of a Salient-recess Junction Explored through a Combined Approach of Field Data and Analog Sandbox Modeling
06:55

Kinematic History of a Salient-recess Junction Explored through a Combined Approach of Field Data and Analog Sandbox Modeling

Published on: August 5, 2016

8.6K

SEDA: A software package for the Statistical Earthquake Data Analysis.

A M Lombardi1

  • 1Istituto Nazionale di Geofisica e Vulcanologia, Via di Vigna Murata 605, 00143 Rome, Italy.

Scientific Reports
|March 15, 2017
PubMed
Summary
This summary is machine-generated.

This paper introduces SEDA v1.0, a new software for seismologists to statistically analyze earthquake data. It enhances research reproducibility and provides fast, accurate earthquake modeling and forecasting tools.

More Related Videos

Data Processing Methods for 3D Seismic Imaging of Subsurface Volcanoes: Applications to the Tarim Flood Basalt
07:58

Data Processing Methods for 3D Seismic Imaging of Subsurface Volcanoes: Applications to the Tarim Flood Basalt

Published on: August 7, 2017

10.0K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K

Related Experiment Videos

Last Updated: Mar 6, 2026

Kinematic History of a Salient-recess Junction Explored through a Combined Approach of Field Data and Analog Sandbox Modeling
06:55

Kinematic History of a Salient-recess Junction Explored through a Combined Approach of Field Data and Analog Sandbox Modeling

Published on: August 5, 2016

8.6K
Data Processing Methods for 3D Seismic Imaging of Subsurface Volcanoes: Applications to the Tarim Flood Basalt
07:58

Data Processing Methods for 3D Seismic Imaging of Subsurface Volcanoes: Applications to the Tarim Flood Basalt

Published on: August 7, 2017

10.0K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K

Area of Science:

  • Seismology
  • Computational Geophysics

Background:

  • The increasing need for research reproducibility in scientific fields.
  • The demand for efficient tools for statistical analysis of earthquake data.

Purpose of the Study:

  • To present the first version of SEDA software (SEDAv1.0).
  • To provide seismologists with a tool for statistically analyzing earthquake data, focusing on ETAS modeling.
  • To ensure research reproducibility and deliver accurate, fast outputs.

Main Methods:

  • Development of a user-friendly Matlab-based interface for easy interaction.
  • Integration of a high-speed computational core using Fortran codes.
  • Implementation of a comprehensive set of tools for Epidemic-Type Aftershock Sequence (ETAS) modeling.

Main Results:

  • SEDAv1.0 offers tools for ETAS parameter estimation, model testing, catalog simulation, sequence identification, and forecast calculation.
  • The software is designed for speed and accuracy, facilitating reproducible seismological research.
  • Initial focus is on ETAS modeling capabilities, with graphical improvements planned for future versions.

Conclusions:

  • SEDAv1.0 is a valuable new software package for seismologists, enhancing the analysis of earthquake data.
  • The software supports the growing movement towards research reproducibility in seismology.
  • Future development will focus on improving the software's graphical interface and expanding its functionalities.