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?

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...
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...
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...
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.
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...
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...

You might also read

Related Articles

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

Sort by
Same author

Community-Based Mental Health Care in Britain.

Consortium psychiatricum·2024
Same author

Cost and quality-of-life impacts of community treatment orders (CTOs) for patients with psychosis: economic evaluation of the OCTET trial.

Social psychiatry and psychiatric epidemiology·2020
Same author

Towards a history of antipsychiatry - Author's reply.

The lancet. Psychiatry·2020
Same author

A history of antipsychiatry in four books.

The lancet. Psychiatry·2020
Same author

Applying the triple bottom line of sustainability to healthcare research-a feasibility study.

International journal for quality in health care : journal of the International Society for Quality in Health Care·2019
Same author

Basaglia's impact - Author's reply.

The lancet. Psychiatry·2019

Related Experiment Video

Updated: May 19, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Where are the hypotheses when you need them?

Tom Burns

    The British Journal of Psychiatry : the Journal of Mental Science
    |September 5, 2012
    PubMed
    Summary
    This summary is machine-generated.

    UK mental health staff face low morale, particularly in acute wards and community teams. Honesty about expectations and reduced criticism may be more effective than complex models for improving staff well-being.

    More Related Videos

    Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
    05:10

    Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

    Published on: December 11, 2016

    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

    Related Experiment Videos

    Last Updated: May 19, 2026

    Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
    05:47

    Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

    Published on: June 13, 2025

    Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
    05:10

    Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

    Published on: December 11, 2016

    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

    Area of Science:

    • Healthcare Management
    • Organizational Psychology
    • Mental Health Services Research

    Background:

    • Poor staff morale is a significant issue within UK mental health services.
    • This problem is particularly acute in inpatient psychiatric wards, community mental health teams, and among social workers.
    • Existing frameworks like the demand-control-support model may not fully address the root causes.

    Discussion:

    • The abstract critiques the application of the demand-control-support model for understanding low staff morale.
    • It proposes alternative, potentially more effective strategies focused on realistic expectations and reduced organizational disruption.
    • This suggests a need to re-evaluate current management approaches in mental healthcare.

    Key Insights:

    • Direct communication regarding job expectations is crucial for staff.
    • Minimizing constant criticism and frequent reorganizations could positively impact morale.
    • A simpler, more transparent approach may yield better results than complex theoretical models.

    Outlook:

    • Further research should investigate the impact of transparent communication and stable work environments on staff morale in mental health settings.
    • Exploring practical, less resource-intensive interventions for improving workplace well-being is recommended.
    • This perspective could inform policy changes aimed at enhancing the retention and effectiveness of mental health professionals.