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

Principle of Equivalence01:18

Principle of Equivalence

According to Albert Einstein (1897-1955), free-falling and feeling weightless are intrinsically linked. If a person were in free-fall under gravity, for example, diving towards the Earth from an airplane, they would feel completely weightless. Similarly, a person descending in a lift may feel partially weightless. Broadly speaking, it is assumed that an object in a uniform gravitational field and an object undergoing constant acceleration in the absence of gravity are under the same...
The Uncertainty Principle04:08

The Uncertainty Principle

Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He mathematically...
The Availability Heuristic01:08

The Availability Heuristic

A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
Decision Making: P-value Method01:09

Decision Making: P-value Method

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 have a...
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...
Null and Alternative Hypotheses01:16

Null and Alternative Hypotheses

The actual hypothesis testing begins by considering two hypotheses. They are termed  the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints.
The null hypothesis, denoted by H0 is a statement of no difference between the variables—they are not related. This can often be considered the status quo. As  a result if you cannot accept the null, it requires some action.
The alternative hypothesis, denoted by H1 or Ha, is a claim about the population that is...

You might also read

Related Articles

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

Sort by
Same author

Is life unique?

Life (Basel, Switzerland)·2014
Same author

Redundancy of the genetic code enables translational pausing.

Frontiers in genetics·2014
Same author

Dichotomy in the definition of prescriptive information suggests both prescribed data and prescribed algorithms: biosemiotics applications in genomic systems.

Theoretical biology & medical modelling·2012
Same author

The capabilities of chaos and complexity.

International journal of molecular sciences·2009
Same author

The GS (genetic selection) Principle.

Frontiers in bioscience (Landmark edition)·2009
Same author

Measuring the functional sequence complexity of proteins.

Theoretical biology & medical modelling·2007

Related Experiment Video

Updated: Jun 18, 2026

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
07:34

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Published on: June 3, 2013

The Universal Plausibility Metric (UPM) & Principle (UPP).

David L Abel1

  • 1Department of ProtoBioCybernetics/ProtoBioSemiotics, The Gene Emergence Project of The Origin of Life Science Foundation, Inc, 113-120 Hedgewood Dr, Greenbelt, MD 20770-1610, USA. life@us.net

Theoretical Biology & Medical Modelling
|December 5, 2009
PubMed
Summary

Scientific plausibility can now be objectively measured using the Universal Plausibility Metric (UPM) and falsified with the Universal Plausibility Principle (UPP). This provides a rigorous standard for evaluating chance hypotheses, especially in origin-of-life models.

Related Experiment Videos

Last Updated: Jun 18, 2026

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
07:34

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Published on: June 3, 2013

Area of Science:

  • Astrobiology
  • Origin of Life Research
  • Scientific Methodology

Background:

  • Scientific plausibility requires more than mere possibility, necessitating a defined bound for operational falsification.
  • Quantifying subjective plausibility is challenging but achievable.
  • The Universal Plausibility Metric (UPM) and Universal Plausibility Principle (UPP) offer objective measures.

Purpose of the Study:

  • To introduce a quantifiable method for assessing the plausibility of chance hypotheses.
  • To establish a universal standard for falsifying hypotheses based on their plausibility.
  • To address the need for objective evaluation in fields like origin-of-life research.

Main Methods:

  • Development of the Universal Plausibility Metric (UPM) for objective measurement.
  • Introduction of the Universal Plausibility Principle (UPP) with a falsification inequality (xi < 1).
  • Demonstration that UPM and UPP are independent of specific experimental designs and data.

Main Results:

  • A method for objectively measuring the plausibility of any chance hypothesis (UPM) is presented.
  • A numerical inequality (UPP) is provided for definitive falsification of chance hypotheses (UPM metric xi < 1).
  • The UPM and UPP are shown to be universally applicable and pre-existing.

Conclusions:

  • No low-probability hypothetical plausibility assertion should pass peer-review without formal falsification using the UPP inequality (xi < 1).
  • The UPM and UPP provide a critical standard for scientific rigor in hypothesis evaluation.
  • These principles are essential for advancing fields reliant on chance-based models, such as abiogenesis.