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

What is a Hypothesis?01:14

What is a Hypothesis?

18.0K
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...
18.0K
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

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

Types of Hypothesis Testing

30.3K
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...
30.3K
Hypothesis: Accept or Fail to Reject?01:17

Hypothesis: Accept or Fail to Reject?

30.2K
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...
30.2K
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

708
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%...
708
Null and Alternative Hypotheses01:16

Null and Alternative Hypotheses

13.7K
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...
13.7K

You might also read

Related Articles

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

Sort by
Same author

Three more steps toward better science.

F1000Research·2019
Same author

Manipulating the Alpha Level Cannot Cure Significance Testing.

Frontiers in psychology·2018
Same author

Retract <i>p</i> < 0.005 and propose using JASP, instead.

F1000Research·2018
Same author

Commentary: Psychological Science's Aversion to the Null.

Frontiers in psychology·2017
Same author

Commentary: The Need for Bayesian Hypothesis Testing in Psychological Science.

Frontiers in psychology·2017
Same author

Commentary: How Bayes factors change scientific practice.

Frontiers in psychology·2016

Related Experiment Video

Updated: Apr 16, 2026

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

4.5K

Fisher, Neyman-Pearson or NHST? A tutorial for teaching data testing.

Jose D Perezgonzalez1

  • 1Business School, Massey University Palmerston North, New Zealand.

Frontiers in Psychology
|March 19, 2015
PubMed
Summary

This tutorial addresses null hypothesis significance testing (NHST), a common but controversial statistical procedure. It explains Fisher's and Neyman-Pearson's approaches and their combination in NHST, offering improvements.

Keywords:
FisherNHSTNeyman-Pearsonnull hypothesis significance testingstatistical educationteaching statisticstest of significancetest of statistical hypotheses

More Related Videos

Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction
16:23

Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction

Published on: February 26, 2014

15.0K
Breakfast Habits among Schoolchildren in the City of Uruguaiana, Brazil
06:48

Breakfast Habits among Schoolchildren in the City of Uruguaiana, Brazil

Published on: July 29, 2020

5.3K

Related Experiment Videos

Last Updated: Apr 16, 2026

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

4.5K
Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction
16:23

Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction

Published on: February 26, 2014

15.0K
Breakfast Habits among Schoolchildren in the City of Uruguaiana, Brazil
06:48

Breakfast Habits among Schoolchildren in the City of Uruguaiana, Brazil

Published on: July 29, 2020

5.3K

Area of Science:

  • Statistics
  • Research Methodology
  • Scientific Teaching

Background:

  • Null Hypothesis Significance Testing (NHST) is widely used in behavioral, social, and biomedical research.
  • Despite calls for reform, NHST remains entrenched in current research practices and education.
  • The optimal time to influence statistical practices is during the teaching of hypothesis testing procedures.

Purpose of the Study:

  • To provide a tutorial for teaching data testing procedures, including Fisher's tests of significance and Neyman-Pearson's tests of acceptance.
  • To explain the combination of these approaches into the current NHST framework.
  • To offer practical solutions for improving NHST for researchers who continue to use it.

Main Methods:

  • Introduction to Fisher's approach to significance testing.
  • Explanation of Neyman-Pearson's approach to acceptance testing.
  • Analysis of the integration of these methods into NHST and proposed improvements.

Main Results:

  • The tutorial clarifies the distinct origins and components of significance testing and acceptance testing.
  • It highlights the incongruent combination leading to current NHST practices.
  • Two compromise solutions for enhancing NHST are presented.

Conclusions:

  • Understanding the historical development of hypothesis testing is crucial for effective statistical practice.
  • The current NHST framework can be understood and potentially improved by examining its constituent parts.
  • Educating researchers on different hypothesis testing theories can lead to more informed and robust data analysis.