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

Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

1.1K
The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
1.1K
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

665
The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and...
665
McNemar's Test01:23

McNemar's Test

1.0K
McNemar's Test is a nonparametric statistical test used to determine if there is a significant difference in proportions between two related groups when the outcome is binary (e.g., yes/no, success/failure). It is beneficial when we have paired data, such as pre-test/post-test designs, where the same subjects are measured under two different conditions. The test is named after the statistician Quinn McNemar, who introduced it in 1947. It is commonly used in situations where subjects are...
1.0K
Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

483
The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in...
483
Wilcoxon Signed-Ranks Test for Median of Single Population01:14

Wilcoxon Signed-Ranks Test for Median of Single Population

550
The Wilcoxon signed-rank test for the median of a single population is a nonparametric test used to evaluate whether the median of a population differs from a specified value. Unlike parametric tests, it does not require data to follow a normal distribution, making it suitable for non-normal or small samples. The test begins by calculating the difference (d) between each observation and the hypothesized median. The absolute values of these differences are ranked in ascending order, with ties...
550
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

You might also read

Related Articles

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

Sort by
Same author

Population codes for context-dependent decision-making.

Current opinion in neurobiology·2026
Same author

Reducing write amplification of DM-SMR disks by shingle-aware persistent cache.

Scientific reports·2026
Same author

Changes in preoperative serum immunoglobulin concentrations in dental patients receiving long-term anticonvulsant therapy.

Journal of dental anesthesia and pain medicine·2026
Same author

Anterior lateral motor cortex enables contextual decision-making via dynamic reconfiguration of local circuits.

Cell reports·2026
Same author

Conscious and nonconscious thought: Insights from the neuroscience of decision-making.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Lipidomic and machine learning analysis reveals enantioselective mechanisms of hexaconazole-induced lipid metabolism disorder in 3T3-L1 preadipocytes.

Archives of toxicology·2026

Related Experiment Video

Updated: Apr 17, 2026

Testing for Metacognitive Responding Using an Odor-based Delayed Match-to-Sample Test in Rats
08:06

Testing for Metacognitive Responding Using an Odor-based Delayed Match-to-Sample Test in Rats

Published on: June 18, 2018

7.8K

A neural implementation of Wald's sequential probability ratio test.

Shinichiro Kira1, Tianming Yang2, Michael N Shadlen3

  • 1Neurobiology & Behavior Program, University of Washington, Seattle, WA 98195, USA; Department of Neuroscience, Columbia University, College of Physicians and Surgeons, New York, NY 10032, USA.

Neuron
|February 10, 2015
PubMed
Summary
This summary is machine-generated.

The brain approximates evidence accumulation using logarithms of likelihood ratios (logLR) for difficult decisions. Monkey studies show lateral intraparietal area (LIP) neuron activity reflects this logLR accumulation to a threshold.

More Related Videos

A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.3K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

8.2K

Related Experiment Videos

Last Updated: Apr 17, 2026

Testing for Metacognitive Responding Using an Odor-based Delayed Match-to-Sample Test in Rats
08:06

Testing for Metacognitive Responding Using an Odor-based Delayed Match-to-Sample Test in Rats

Published on: June 18, 2018

7.8K
A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.3K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

8.2K

Area of Science:

  • Neuroscience
  • Cognitive Science
  • Decision Making

Background:

  • Difficult decisions involve sequential evidence evaluation.
  • Optimal statistical strategies accumulate evidence weighted by reliability.
  • The brain may approximate this by accumulating logarithms of likelihood ratios (logLR).

Purpose of the Study:

  • To directly test evidence accumulation in units of logLR to a threshold.
  • To investigate the neural basis of sequential evidence accumulation in decision-making.

Main Methods:

  • Trained rhesus monkeys on a decision-making task using sequential visual cues.
  • Assigned different logLR values to cues, varying their reliability.
  • Recorded neural activity in the lateral intraparietal area (LIP).

Main Results:

  • LIP neuronal firing rates reflected the accumulation of logLR.
  • Neural activity reached a stereotyped level before decision commitment.
  • Monkey choices and reaction times were explained by LIP activity and logLR accumulation to a threshold.

Conclusions:

  • The brain's decision-making process approximates optimal statistical accumulation of logLR.
  • LIP neuronal activity provides a neural correlate for cumulative logLR in perceptual decisions.
  • This provides direct evidence for accumulation to a threshold in units of logLR.