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

428
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,...
428
Introduction to Nonparametric Statistics01:28

Introduction to Nonparametric Statistics

1.3K
Nonparametric statistics offer a powerful alternative to traditional parametric methods, useful when assumptions about the population distribution cannot be made. Unlike parametric tests, which require data to follow a specific distribution with well-defined parameters (such as the mean and standard deviation), nonparametric tests do not require such constraints. This makes them particularly valuable when dealing with small sample sizes, skewed data, or ordinal and categorical variables.
One of...
1.3K
Survival Tree01:19

Survival Tree

374
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
374
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

990
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
990
Statistical Methods to Analyze Parametric Data: ANOVA01:12

Statistical Methods to Analyze Parametric Data: ANOVA

1.5K
Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares...
1.5K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

223
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
223

You might also read

Related Articles

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

Sort by
Same author

Multinomial models of the repetition-based truth effect: Investigating the role of prior knowledge.

Memory & cognition·2026
Same author

Do all subjects fit the same recognition memory model? Comparisons of continuous, discrete, and hybrid models using extended multinomial processing trees.

Journal of experimental psychology. Learning, memory, and cognition·2026
Same author

The Interval Consensus Model: Aggregating Continuous Bounded Interval Responses.

Psychometrika·2025
Same author

Modeling the link between the plausibility of statements and the truth effect.

Psychonomic bulletin & review·2025
Same author

Cheat, cheat, repeat: On the consistency of dishonest behavior in structurally comparable situations.

Journal of personality and social psychology·2025
Same author

Modeling dependent group judgments: A computational model of sequential collaboration.

Psychonomic bulletin & review·2025
Same journal

Exploring psychological tradeoffs: Developing and demonstrating an R Shiny app for Pareto optimization.

Behavior research methods·2026
Same journal

The performance of Bayesian fit measures in detecting misspecified multilevel structural equation modeling.

Behavior research methods·2026
Same journal

Psychometric functions from multiple responses : Dedicated to the memory of Colin L. Mallows.

Behavior research methods·2026
Same journal

Low-cost, open-source, full-stack software and Arduino-based hardware for control of commercially available animal behavior systems.

Behavior research methods·2026
Same journal

PyNeon: A Python package for the analysis of Neon multimodal mobile eye-tracking data.

Behavior research methods·2026
Same journal

Talking surveys: How photorealistic embodied conversational agents shape response quality, engagement, and satisfaction.

Behavior research methods·2026
See all related articles

Related Experiment Video

Updated: Jan 9, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.7K

Extensions of multinomial processing tree models for continuous variables: A simulation study comparing parametric

Anahí Gutkin1, Daniel W Heck2

  • 1Faculty of Medicine, Universidad Francisco de Vitoria, Madrid, Spain. anahi.gutkin@ufv.es.

Behavior Research Methods
|December 9, 2025
PubMed
Summary
This summary is machine-generated.

Parametric multinomial processing tree (MPT) models offer higher statistical power for analyzing response times but are sensitive to distributional assumptions. Non-parametric MPT models are more robust but less powerful, especially with limited data.

Keywords:
Cognitive modelingMPT modelsNon-parametric approachesResponse times

More Related Videos

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.8K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.4K

Related Experiment Videos

Last Updated: Jan 9, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.7K
Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.8K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.4K

Area of Science:

  • Cognitive Psychology
  • Quantitative Psychology
  • Psychometric Methods

Background:

  • Multinomial processing tree (MPT) models are used for analyzing discrete and continuous variables.
  • Parametric and non-parametric extensions of MPT models exist but lack systematic comparison.
  • The weapon identification task provides a context for evaluating these MPT model extensions.

Purpose of the Study:

  • To systematically compare the statistical power and robustness of parametric and non-parametric MPT models.
  • To evaluate the performance of goodness-of-fit tests for parametric MPT models.
  • To assess model recovery for nested and non-nested MPT models.

Main Methods:

  • Three simulation studies were conducted using the weapon identification task.
  • Simulations manipulated discrepancies in latent response time (RT) distributions, sample size, and parametric assumptions.
  • Evaluated calibration, statistical power, and model recovery for parametric and non-parametric MPT approaches.

Main Results:

  • Parametric MPT models demonstrated higher statistical power but were sensitive to distributional assumption misspecifications.
  • Non-parametric MPT models showed greater robustness but lower power, particularly with small sample sizes.
  • Model recovery varied based on model complexity, sample size, and discrepancy type.

Conclusions:

  • The choice between parametric and non-parametric MPT models depends on the specific research context, sample size, and data characteristics.
  • Parametric models are suitable when distributional assumptions are met and high power is needed.
  • Non-parametric models offer a more robust alternative when distributional assumptions are uncertain or violated.