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

488
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,...
488
Statistical Significance01:50

Statistical Significance

21.8K
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...
21.8K
Rationalizing Substitutions01:29

Rationalizing Substitutions

59
Integrals involving non-rational functions are often difficult to evaluate using standard techniques, especially when radicals appear in the integrand. Rationalizing substitution provides a systematic method for simplifying such integrals by converting them into rational forms that are easier to handle.Consider a rod whose linear mass density depends on a constant linear density, a characteristic length, and the distance from the left end of the rod. Determining the total mass requires...
59
Rational Expressions01:28

Rational Expressions

392
Rational expressions are algebraic fractions in which both the numerator and the denominator are polynomials. These expressions follow the arithmetic rules of numerical fractions but require extra care due to the presence of variables. A fundamental part of working with rational expressions is identifying values that make the expression undefined, typically those that result in division by zero or undefined radicals.Determining the DomainThe domain of a rational expression includes all real...
392
Asymptotes in Rational Functions01:30

Asymptotes in Rational Functions

252
A rational function is defined as the quotient of two polynomials:  where Q(x)≠0, These functions often exhibit asymptotes, which are the lines that the graph approaches but never touches. These asymptotes are classified based on how the function behaves near specific values of the input.Vertical asymptotes occur where the denominator is zero, and the numerator is not, causing the function to be undefined. These are found by solving Q(x)=0. For example:  has a vertical...
252
Probability in Statistics01:14

Probability in Statistics

23.4K
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...
23.4K

You might also read

Related Articles

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

Sort by
Same author

Reproducibility and validity of an FFQ developed for adults in Nanjing, China.

The British journal of nutrition·2016
Same author

[Chemical Constituents from Leaves of Hibiscus syriacus and Their α-Glucosidase Inhibitory Activities].

Zhong yao cai = Zhongyaocai = Journal of Chinese medicinal materials·2016
Same author

Quantifying Different Tactile Sensations Evoked by Cutaneous Electrical Stimulation Using Electroencephalography Features.

International journal of neural systems·2016
Same author

A Highly Sensitive Immunosorbent Assay Based on Biotinylated Graphene Oxide and the Quartz Crystal Microbalance.

ACS applied materials & interfaces·2016
Same author

Identification of Thyroid Hormones and Functional Characterization of Thyroid Hormone Receptor in the Pacific Oyster Crassostrea gigas Provide Insight into Evolution of the Thyroid Hormone System.

PloS one·2015
Same author

Decreased functional connectivity density in pain-related brain regions of female migraine patients without aura.

Brain research·2015

Related Experiment Video

Updated: Feb 2, 2026

Genotypic Inference of HIV-1 Tropism Using Population-based Sequencing of V3
11:10

Genotypic Inference of HIV-1 Tropism Using Population-based Sequencing of V3

Published on: December 27, 2010

12.8K

Another Look at Looking Time: Surprise as Rational Statistical Inference.

Zi L Sim1, Fei Xu1

  • 1Department of Psychology, University of California, Berkeley.

Topics in Cognitive Science
|November 10, 2018
PubMed
Summary

Infant looking time, a measure of surprise, may reflect sophisticated statistical inference rather than just perceptual discrimination or violated expectations. This new perspective offers insights into early cognitive development and learning processes.

Keywords:
Cognitive developmentInfant cognitionLooking timeRational statistical inferenceSurpriseViolation-of-expectation method

More Related Videos

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.1K
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.6K

Related Experiment Videos

Last Updated: Feb 2, 2026

Genotypic Inference of HIV-1 Tropism Using Population-based Sequencing of V3
11:10

Genotypic Inference of HIV-1 Tropism Using Population-based Sequencing of V3

Published on: December 27, 2010

12.8K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.1K
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.6K

Area of Science:

  • Developmental Psychology
  • Cognitive Science
  • Infant Cognition

Background:

  • Infant looking time differences indicate surprise, traditionally interpreted as perceptual discrimination or violated expectations.
  • Decades of research utilize infant surprise as a key indicator in developmental studies.

Purpose of the Study:

  • To present a novel perspective on infant surprise, focusing on the underlying cognitive processes.
  • To propose that infant looking time reflects sophisticated statistical inference capabilities.

Main Methods:

  • Review of empirical evidence from recent developmental studies.
  • Analysis of computational modeling results supporting the statistical inference hypothesis.
  • Integration with existing research on infant surprise and learning.

Main Results:

  • Empirical data and computational models support the conjecture that infant looking time reflects statistical inference.
  • This perspective offers a more nuanced understanding of cognitive processes driving infant surprise.

Conclusions:

  • Infant surprise, as measured by looking time, is proposed to be a manifestation of advanced statistical learning.
  • This framework reframes our understanding of early cognitive development and learning mechanisms.
  • Future research directions are outlined to further explore this statistical inference perspective.