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

Types of Hypothesis Testing01:11

Types of Hypothesis Testing

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 ≠ 0.5.
Null and Alternative Hypotheses01:16

Null and Alternative Hypotheses

The actual hypothesis testing begins by considering two hypotheses. They are termed  the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints.
The null hypothesis, denoted by H0 is a statement of no difference between the variables—they are not related. This can often be considered the status quo. As  a result if you cannot accept the null, it requires some action.
The alternative hypothesis, denoted by H1 or Ha, is a claim about the population that is...
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

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...
What is a Hypothesis?01:14

What is a Hypothesis?

A hypothesis can be a simple sentence or statement about a property or any phenomenon observed or predicted for a population. It is usually a claim about a  property of the population. It can be stated for any field observations or experiments. A hypothesis statement cannot be said to be right or wrong as it is merely a statement. It needs to be tested through an elaborate data collection process and an appropriate statistical test. A hypothesis should be a general but not a vague statement. It...
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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...
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...

You might also read

Related Articles

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

Sort by
Same author

The Impact of Shift Work on Diabetes Self-Management Activities.

Journal of doctoral nursing practice·2020
Same author

None of the above: A Bayesian account of the detection of novel categories.

Psychological review·2017
Same author

Not every credible interval is credible: Evaluating robustness in the presence of contamination in Bayesian data analysis.

Behavior research methods·2017
Same author

Bayesian models of cognition revisited: Setting optimality aside and letting data drive psychological theory.

Psychological review·2017
Same author

On the likelihood of "encapsulating all uncertainty".

Science & justice : journal of the Forensic Science Society·2017
Same author

Structure at every scale: A semantic network account of the similarities between unrelated concepts.

Journal of experimental psychology. General·2016
Same journal

Why the Big Five personality traits are composites, not common causes: Implications for measurement, prediction, and causal inference.

Psychological review·2026
Same journal

Perception and action as one: Re-integrating research on human action through event files.

Psychological review·2026
Same journal

Associative learning explains "intuitive statistics" in animals.

Psychological review·2026
Same journal

A reciprocal model of practice and skill: Navigating between dropout and expertise.

Psychological review·2026
Same journal

The relative psychometric function: A general analysis framework for relating psychological processes.

Psychological review·2026
Same journal

A taxonomy of discriminatory behavior.

Psychological review·2026
See all related articles

Related Experiment Video

Updated: Jun 6, 2026

Barnes Maze Testing Strategies with Small and Large Rodent Models
12:59

Barnes Maze Testing Strategies with Small and Large Rodent Models

Published on: February 26, 2014

Hypothesis generation, sparse categories, and the positive test strategy.

Daniel J Navarro1, Amy F Perfors

  • 1School of Psychology, University of Adelaide, South Australia 5005, Australia. daniel.navarro@adelaide.edu.au

Psychological Review
|November 10, 2010
PubMed
Summary
This summary is machine-generated.

When learning rules from data without a predefined hypothesis list, a positive test strategy is effective for sparse hypotheses. A bias for sparsity naturally arises from the family resemblance principle, favoring similar entities.

More Related Videos

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
07:31

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

Related Experiment Videos

Last Updated: Jun 6, 2026

Barnes Maze Testing Strategies with Small and Large Rodent Models
12:59

Barnes Maze Testing Strategies with Small and Large Rodent Models

Published on: February 26, 2014

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
07:31

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

Area of Science:

  • Cognitive Science
  • Machine Learning
  • Philosophy of Science

Background:

  • Inductive learning involves inferring rules from observed data.
  • The space of potential rules (hypothesis space) is often not explicitly defined.
  • Discovering effective learning strategies in such scenarios is a key challenge.

Purpose of the Study:

  • To analyze the effectiveness of learning strategies when the hypothesis space is not enumerated.
  • To investigate the conditions under which a positive test strategy is near optimal.
  • To explore the emergence of a sparsity bias in rule induction.

Main Methods:

  • Theoretical analysis of learning strategies in an undefined hypothesis space.
  • Demonstration of the near optimality of positive test strategies for sparse hypotheses.
  • Examination of the family resemblance principle's role in developing a sparsity bias.

Main Results:

  • A positive test strategy is near optimal when hypotheses are sparse (index < 50% of domain entities).
  • A preference for sparse hypotheses naturally emerges from the family resemblance principle.
  • Good rules identified by this principle index similar entities.

Conclusions:

  • The study provides insights into efficient rule induction from data.
  • Sparsity bias is a natural outcome of similarity-based learning principles.
  • Positive test strategies are valuable for learning with large or undefined hypothesis spaces.