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

Probability Laws01:49

Probability Laws

43.8K
Overview
43.8K
Probability in Statistics01:14

Probability in Statistics

21.6K
Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
21.6K
Decision Making: P-value Method01:09

Decision Making: P-value Method

6.7K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
6.7K
Probability Distributions01:32

Probability Distributions

11.5K
 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...
11.5K
Binomial Probability Distribution01:15

Binomial Probability Distribution

15.0K
A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
15.0K
Reason and Intuition01:37

Reason and Intuition

7.3K
The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
7.3K

You might also read

Related Articles

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

Sort by
Same author

Deterministic, stochastic, and mean-field PDE models in neuroscience.

Frontiers in computational neuroscience·2026
Same author

Reduced chattering target-tracking sliding mode control for intraprocedural propofol control.

ISA transactions·2025
Same author

Suicide at work: How can this relationship be demonstrated? Action research in a public agency with legal activity.

Work (Reading, Mass.)·2025
Same author

Adding Space to Random Networks of Spiking Neurons: A Method Based on Scaling the Network Size.

Neural computation·2025
Same author

Dynamic nonlinear control strategies for resilient heterogeneous vehicle platooning and handling Byzantine attacks in communication networks.

Heliyon·2025
Same author

Towards optical trapping and enantioselectivity of single biomolecules by interference of collective plasmons.

Optics express·2023

Related Experiment Video

Updated: Dec 24, 2025

Using the Threat Probability Task to Assess Anxiety and Fear During Uncertain and Certain Threat
11:18

Using the Threat Probability Task to Assess Anxiety and Fear During Uncertain and Certain Threat

Published on: September 12, 2014

15.6K

Brumadinho: between prudence and probability, tragedy.

Laerte Idal Sznelwar1, Mauro Zilbovicius1, Cláudio Marcelo Brunoro1

  • 1Polytechnic School, Universidade de São Paulo (USP) - São Paulo (SP), Brazil.

Revista Brasileira De Medicina Do Trabalho : Publicacao Oficial Da Associacao Nacional De Medicina Do Trabalho-Anamt
|April 10, 2020
PubMed
Summary

Engineering aims for safety but absolute certainty in dam failure prevention is impossible due to inherent uncertainties. Risk management in tailings dam engineering must acknowledge and address these unpredictable events.

Keywords:
accidents, occupationalengineeringergonomicsprobabilitystress, psychological

More Related Videos

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

12.4K

Related Experiment Videos

Last Updated: Dec 24, 2025

Using the Threat Probability Task to Assess Anxiety and Fear During Uncertain and Certain Threat
11:18

Using the Threat Probability Task to Assess Anxiety and Fear During Uncertain and Certain Threat

Published on: September 12, 2014

15.6K
Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

12.4K

Area of Science:

  • Geotechnical Engineering
  • Risk Management
  • Civil Engineering

Background:

  • The Brumadinho dam failure highlights the limitations of predicting catastrophic events.
  • Engineering practice often uses
  • exactness
  • as a euphemism for comprehensive understanding and control.

Purpose of the Study:

  • To explore the feasibility of achieving exact explanations for dam failures.
  • To examine the role of absolute knowledge versus empirical know-how in dam safety.
  • To underscore the inherent uncertainties in tailings dam engineering.

Main Methods:

  • Conceptual analysis of engineering knowledge and practice.
  • Discussion of deterministic versus probabilistic approaches to safety.
  • Examination of the concept of "absolute knowledge" in engineering design.

Main Results:

  • Absolute knowledge of all phenomena and potential failure events in dam engineering is unattainable.
  • Traditional engineering relies on empirical knowledge and safety margins, reducing but not eliminating failure risks.
  • Uncertainty is an intrinsic and unavoidable aspect of dam failure events.

Conclusions:

  • Exact prediction and prevention of all dam failures are not currently possible.
  • Tailings dam engineering must operate with an understanding of inherent uncertainties.
  • Risk management strategies should account for the probabilistic nature of failure events.