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 Hypothesis Testing01:16

Statistical Hypothesis Testing

7.1K
Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
7.1K
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

583
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,...
583
Decision Making: P-value Method01:09

Decision Making: P-value Method

7.2K
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...
7.2K
Types of Hypothesis Testing01:11

Types of Hypothesis Testing

29.8K
There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p...
29.8K
Hypothesis: Accept or Fail to Reject?01:17

Hypothesis: Accept or Fail to Reject?

29.9K
The outcome of any hypothesis testing leads to rejecting or not rejecting the null hypothesis. This decision is taken based on the analysis of the data, an appropriate test statistic, an appropriate confidence level, the critical values, and P-values. However, when the evidence suggests that the null hypothesis cannot be rejected, is it right to say, 'Accept' the null hypothesis?
There are two ways to indicate that the null hypothesis is not rejected. 'Accept' the null...
29.9K
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

5.8K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
5.8K

You might also read

Related Articles

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

Sort by
Same author

Waiting in intertemporal choice tasks affects discounting and subjective time perception.

Journal of experimental psychology. General·2020
Same author

Hunger increases delay discounting of food and non-food rewards.

Psychonomic bulletin & review·2019
Same journal

Planned missingness in intensive longitudinal studies: Extensions and comparisons of multiform designs.

Behavior research methods·2026
Same journal

A validity-guided workflow for robust large language model research in psychology.

Behavior research methods·2026
Same journal

Are 7-point Likert scales preferable to 5-point scales in language research?

Behavior research methods·2026
Same journal

Generative psychometrics via AI-GENIE: Automatic item generation and validation with network-integrated evaluation.

Behavior research methods·2026
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
See all related articles

Related Experiment Video

Updated: Mar 30, 2026

Measuring Delay Discounting in Humans Using an Adjusting Amount Task
07:47

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Published on: January 9, 2016

16.1K

Hierarchical Bayesian estimation and hypothesis testing for delay discounting tasks.

Benjamin T Vincent1

  • 1School of Psychology, University of Dundee, Dundee, UK. b.t.vincent@dundee.ac.uk.

Behavior Research Methods
|November 7, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a hierarchical Bayesian analysis for delay discounting data, enhancing reward trade-off understanding. The method improves data efficiency and aids hypothesis testing in behavioral economics and neuroscience.

Keywords:
Bayesian estimationDecision makingDelay discountingFinancial psychophysicsInter-temporal choiceMCMCMagnitude effectTime preference

More Related Videos

Errors as a Means of Reducing Impulsive Food Choice
07:07

Errors as a Means of Reducing Impulsive Food Choice

Published on: June 5, 2016

9.3K
Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

10.4K

Related Experiment Videos

Last Updated: Mar 30, 2026

Measuring Delay Discounting in Humans Using an Adjusting Amount Task
07:47

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Published on: January 9, 2016

16.1K
Errors as a Means of Reducing Impulsive Food Choice
07:07

Errors as a Means of Reducing Impulsive Food Choice

Published on: June 5, 2016

9.3K
Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

10.4K

Area of Science:

  • Behavioral Economics
  • Cognitive Science
  • Neuroscience

Background:

  • Delay discounting is crucial for understanding reward valuation across disciplines.
  • Accurate analysis of behavioral data is essential for reliable inferences.
  • Existing methods may not fully leverage limited or noisy data.

Purpose of the Study:

  • To present a novel hierarchical Bayesian data analysis procedure for delay discounting.
  • To enable robust hypothesis testing on behavioral economic and neuroscience data.
  • To efficiently utilize limited and noisy behavioral data for precise inferences.

Main Methods:

  • Developed a state-of-the-art hierarchical Bayesian inference and hypothesis testing procedure.
  • Utilized a probabilistic generative model for trial-to-trial reward choice valuation.
  • Estimated participant- and group-level parameters, including discount rate as a function of reward size.

Main Results:

  • The analysis allows for rich inferences and confidence measures from behavioral data.
  • Hierarchical modeling enhances precision and data efficiency.
  • The magnitude effect in delay discounting can be effectively measured.

Conclusions:

  • The presented analysis procedure offers a powerful tool for delay discounting research.
  • It facilitates hypothesis testing and precise parameter estimation.
  • The freely available software supports broad application in economics, cognitive science, and neuroscience.