Jove
Visualize
Contact Us

Related Concept Videos

Types of Hypothesis Testing01:11

Types of Hypothesis Testing

27.5K
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...
27.5K
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

457
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%...
457
Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

5.5K
When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
5.5K
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

4.9K
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...
4.9K
Second-order Op Amp Circuits01:19

Second-order Op Amp Circuits

516
Implementing second-order low-pass filters in audio systems is crucial in refining audio signals by eliminating undesirable high-frequency noise. These filters typically involve second-order op-amp circuits configured as voltage followers, encompassing two nodes with distinct storage elements.
The analysis of such circuits follows a systematic approach, similar to the second-order RLC circuits. In practical scenarios, bulky inductors are rarely employed due to their size and weight. This means...
516
Hypothesis Test for Test of Independence01:16

Hypothesis Test for Test of Independence

6.1K
The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
H0: The two variables (factors)...
6.1K

You might also read

Related Articles

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

Sort by
Same author

Optimum Achievable Rates in Two Random Number Generation Problems with <i>f</i>-Divergences Using Smooth Rényi Entropy.

Entropy (Basel, Switzerland)·2024
Same author

Variable-Length Resolvability for General Sources and Channels.

Entropy (Basel, Switzerland)·2023
See all related articles
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 Experiment Video

Updated: Nov 27, 2025

A Real-world What-Where-When Memory Test
09:13

A Real-world What-Where-When Memory Test

Published on: May 16, 2017

11.7K

First- and Second-Order Hypothesis Testing for Mixed Memoryless Sources.

Te Sun Han1, Ryo Nomura2

  • 1National Institute of Information and Communications Technology (NICT), Tokyo 184-8795, Japan.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary
This summary is machine-generated.

This study investigates optimal error exponents in hypothesis testing for memoryless sources. Researchers determined the second-order epsilon-optimum exponent for mixed and stationary sources, advancing information theory analysis.

Keywords:
general sourcehypothesis testinginformation spectrummixed sourceoptimum exponent

More Related Videos

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.8K
A Two-interval Forced-choice Task for Multisensory Comparisons
07:13

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

11.3K

Related Experiment Videos

Last Updated: Nov 27, 2025

A Real-world What-Where-When Memory Test
09:13

A Real-world What-Where-When Memory Test

Published on: May 16, 2017

11.7K
Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.8K
A Two-interval Forced-choice Task for Multisensory Comparisons
07:13

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

11.3K

Area of Science:

  • Information Theory
  • Statistical Inference
  • Hypothesis Testing

Background:

  • Hypothesis testing is fundamental in statistical inference.
  • Error probabilities (Type I and Type II) are key metrics.
  • Asymptotic analysis provides insights into performance limits.

Purpose of the Study:

  • To investigate first- and second-order optimum achievable exponents in simple hypothesis testing.
  • To determine the epsilon-optimum exponent for Type II error under a constrained Type I error probability.
  • To extend these findings to more general source settings.

Main Methods:

  • Analysis of simple hypothesis testing problems.
  • Calculation of second-order epsilon-optimum exponents for mixed and stationary memoryless sources.
  • Generalization to mixed memoryless alternative hypotheses.
  • Investigation of first-order epsilon-optimum exponents.

Main Results:

  • The second-order epsilon-optimum exponent was determined for mixed memoryless null and stationary memoryless alternative hypotheses.
  • The analysis was extended to cases with mixed memoryless alternative hypotheses.
  • The first-order epsilon-optimum exponent was addressed within this framework.

Conclusions:

  • The study provides precise characterizations of error exponents in hypothesis testing.
  • Results advance the understanding of performance limits for mixed and stationary sources.
  • Extensions suggest broader applicability to general compound hypothesis testing.