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

7.2K
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
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
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
Null and Alternative Hypotheses01:16

Null and Alternative Hypotheses

13.8K
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...
13.8K
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

710
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%...
710

You might also read

Related Articles

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

Sort by
Same author

Design, simulate, refine: simulation-guided clinical trials for accelerated drug development.

Nature reviews. Drug discovery·2026
Same author

Pre-Posterior Distributions in Drug Development and Their Properties.

Pharmaceutical statistics·2024
Same author

Probability of success and group sequential designs.

Pharmaceutical statistics·2023
Same author

On implementing Jeffreys' substitution likelihood for Bayesian inference concerning the medians of unknown distributions.

Pharmaceutical statistics·2022
Same author

Optimising the trade-off between type I and II error rates in the Bayesian context.

Pharmaceutical statistics·2021
Same author

How to design a dose-finding study using the continual reassessment method.

BMC medical research methodology·2019

Related Experiment Video

Updated: Apr 18, 2026

Establishment of Rat Models Mimicking Gender-affirming Hormone Therapies
06:24

Establishment of Rat Models Mimicking Gender-affirming Hormone Therapies

Published on: January 10, 2025

1.8K

How to test hypotheses if you must.

Andrew P Grieve1

  • 1ICON Adaptive Trials Innovation Centre, Icon Plc, Marlow, Buckinghamshire, UK.

Pharmaceutical Statistics
|February 3, 2015
PubMed
Summary

This study explores regulated statistical methods in drug development and ecology. It shows that considering error costs in significance testing aligns with the likelihood principle and Bayesian methods.

Keywords:
Bayesian testLindley's paradoxNeyman-Pearson lemmahypothesis testslikelihood principlenull hypothesis significance testsplanning of experimentspowersample sizingsampling frametype I errortype II error

More Related Videos

An Olfactory Preference Test for Measuring Olfactory Hedonic Biases in Mouse Models of Depression
06:27

An Olfactory Preference Test for Measuring Olfactory Hedonic Biases in Mouse Models of Depression

Published on: July 11, 2025

1.2K
Hypoxia Alters miRNAs Levels Involved in Non-Mendelian Inheritance of Autism Spectrum Disorder in Mice
09:13

Hypoxia Alters miRNAs Levels Involved in Non-Mendelian Inheritance of Autism Spectrum Disorder in Mice

Published on: July 11, 2025

35.6K

Related Experiment Videos

Last Updated: Apr 18, 2026

Establishment of Rat Models Mimicking Gender-affirming Hormone Therapies
06:24

Establishment of Rat Models Mimicking Gender-affirming Hormone Therapies

Published on: January 10, 2025

1.8K
An Olfactory Preference Test for Measuring Olfactory Hedonic Biases in Mouse Models of Depression
06:27

An Olfactory Preference Test for Measuring Olfactory Hedonic Biases in Mouse Models of Depression

Published on: July 11, 2025

1.2K
Hypoxia Alters miRNAs Levels Involved in Non-Mendelian Inheritance of Autism Spectrum Disorder in Mice
09:13

Hypoxia Alters miRNAs Levels Involved in Non-Mendelian Inheritance of Autism Spectrum Disorder in Mice

Published on: July 11, 2025

35.6K

Area of Science:

  • Ecology
  • Environmental Science
  • Drug Development
  • Statistical Methodology

Background:

  • Government regulations impact industrial-scientific endeavors beyond drug development, including ecological and environmental sciences.
  • There's a long-standing debate among ecologists against the uncritical use of null hypothesis significance tests.
  • Recent Canadian ecological research proposes a novel significance testing approach incorporating Type I and Type II error costs.

Purpose of the Study:

  • To investigate the implications of a new ecological significance testing approach for drug development.
  • To demonstrate how this approach connects to established statistical principles in pharmaceutical research.

Main Methods:

  • Comparative analysis of statistical testing frameworks.
  • Exploration of error cost considerations in significance testing.
  • Theoretical investigation of implications for drug development protocols.

Main Results:

  • The proposed ecological significance testing method, accounting for error costs, directly leads to the likelihood principle.
  • Adoption of this approach in drug development naturally integrates Bayesian statistical perspectives.
  • This highlights a convergence between ecological statistical innovations and pharmaceutical research methodologies.

Conclusions:

  • The integration of error cost analysis in significance testing offers a unified framework for ecological and drug development statistics.
  • This approach supports a move towards more nuanced and cost-aware statistical decision-making in regulated scientific fields.
  • The likelihood principle and Bayesian methods emerge as natural consequences of this error-conscious testing paradigm.