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

Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

8.8K
On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
8.8K
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.5K
When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
3.5K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

9.3K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
9.3K
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

10.1K
To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
10.1K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

1.5K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
1.5K
Area Computation by the Alternative Coordinate Method01:24

Area Computation by the Alternative Coordinate Method

832
The alternative coordinate method, also known as the Shoelace Formula, is a technique for determining the area of a traverse using Cartesian coordinates. This method relies on the sequential arrangement of x and y coordinates for each point of the shape, ensuring accuracy and ease of application.In this approach, each corner's x and y coordinates are listed as fractions, with the x-coordinate as the numerator and the y-coordinate as the denominator. These coordinates are arranged sequentially...
832

You might also read

Related Articles

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

Sort by
Same author

Distributed control circuits across a brain-and-cord connectome.

Nature·2026
Same author

Peripheral anatomy and central connectivity of proprioceptive sensory neurons in the <i>Drosophila</i> wing.

eLife·2026
Same author

Special days for one group inform children about both mentioned and unmentioned groups.

Journal of experimental child psychology·2026
Same author

Context Matters: "Left Digit" Effects Can Arise for Digits That are Not Leftmost.

Quarterly journal of experimental psychology (2006)·2025
Same author

Distributed control circuits across a brain-and-cord connectome.

bioRxiv : the preprint server for biology·2025
Same author

Peripheral anatomy and central connectivity of proprioceptive sensory neurons in the <i>Drosophila</i> wing.

bioRxiv : the preprint server for biology·2025
Same journal

Correction to "Navigating Knowledge: Effects of State Curiosity on Children's Word Learning and Information Seeking".

Developmental science·2026
Same journal

Developmental Unity and Cultural Variation in Forms of Metarepresentational False Belief Understanding.

Developmental science·2026
Same journal

Cascading Periods of Language-Related Brain Plasticity Across Early Childhood.

Developmental science·2026
Same journal

Recognition of Familiar Wordforms and Phonological Variation in Akan by Multilingual Infants Learning African Tone Languages in Ghana.

Developmental science·2026
Same journal

Correction to "ManyNumbers 3: A Multi-Lab Study of Demographic Correlates of Early Number Knowledge".

Developmental science·2026
Same journal

Exploring Variation in Infants' Preference for Infant-directed Speech: Evidence From a Multi-Site Study in Africa.

Developmental science·2026
See all related articles

Related Experiment Video

Updated: Apr 20, 2026

Topographical Estimation of Visual Population Receptive Fields by fMRI
06:02

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

9.8K

Spatial estimation: a non-Bayesian alternative.

Hilary Barth1, Ellen Lesser1, Jessica Taggart1

  • 1Department of Psychology, Wesleyan University, USA.

Developmental Science
|December 3, 2014
PubMed
Summary
This summary is machine-generated.

Complex Bayesian models may not fully explain spatial estimation biases. A simpler, non-Bayesian psychophysical model effectively explains biases in adults and children, suggesting an alternative to complex Bayesian approaches for understanding memory and estimation.

More Related Videos

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

15.4K
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

3.1K

Related Experiment Videos

Last Updated: Apr 20, 2026

Topographical Estimation of Visual Population Receptive Fields by fMRI
06:02

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

9.8K
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

15.4K
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

3.1K

Area of Science:

  • Cognitive Psychology
  • Developmental Psychology
  • Human Perception

Background:

  • Spatial estimation biases in adults and children are often explained using complex Bayesian models.
  • These models attempt to account for phenomena like errors in remembering object locations within bounded spaces.
  • Existing explanations may overlook simpler underlying mechanisms.

Purpose of the Study:

  • To investigate whether a simpler, non-Bayesian model can explain spatial estimation biases.
  • To compare the explanatory power of a psychophysical proportion estimation model against complex Bayesian models.
  • To examine estimation behaviors in both adult and child populations.

Main Methods:

  • Participants, including undergraduates and 9- to 10-year-olds, completed a speeded linear position estimation task.
  • Data on estimation biases were collected and analyzed.
  • The study employed a simple psychophysical model of proportion estimation for analysis.

Main Results:

  • Biases in linear position estimation for both age groups were successfully explained by the simple psychophysical model.
  • The non-Bayesian model provided a viable alternative to complex Bayesian explanations.
  • Some individual data patterns were inconsistent with the predictions of complex Bayesian models.

Conclusions:

  • A simpler, non-Bayesian psychophysical model can account for certain spatial estimation biases.
  • This finding challenges the necessity of complex Bayesian models for all estimation phenomena.
  • The results suggest a need to reconsider the theoretical frameworks used to explain human spatial memory and estimation.