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Related Concept Videos

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

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Related Experiment Video

Updated: Jul 5, 2026

A Method to Test the Efficacy of Handwashing for the Removal of Emerging Infectious Pathogens
09:02

A Method to Test the Efficacy of Handwashing for the Removal of Emerging Infectious Pathogens

Published on: June 7, 2017

Consumer health information seeking as hypothesis testing.

Alla Keselman1, Allen C Browne, David R Kaufman

  • 1Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institute of Health, Bethesda, MD, USA. keselmana@mail.nih.gov

Journal of the American Medical Informatics Association : JAMIA
|April 26, 2008
PubMed
Summary

Lay individuals struggle to find reliable online health information due to flawed understanding of medical concepts. Even with good search skills, incorrect knowledge can lead to inefficient searches for health data.

Related Experiment Videos

Last Updated: Jul 5, 2026

A Method to Test the Efficacy of Handwashing for the Removal of Emerging Infectious Pathogens
09:02

A Method to Test the Efficacy of Handwashing for the Removal of Emerging Infectious Pathogens

Published on: June 7, 2017

Area of Science:

  • Health Informatics
  • Information Science
  • Consumer Health Information

Background:

  • Consumer health websites are abundant, yet users face challenges accessing relevant information.
  • Understanding user difficulties in online health information seeking is crucial.

Purpose of the Study:

  • To investigate the difficulties lay individuals encounter when searching for health information online.
  • To explore the influence of user competencies and internet resources on information seeking.
  • To apply a hypothesis testing framework to understand these challenges.

Main Methods:

  • Twenty participants were interviewed regarding a hypothetical health scenario (stable angina).
  • Participants searched the MedlinePlus consumer health portal.
  • Semantic analysis of participants' understanding of heart disease and thematic coding of search strategies were employed.

Main Results:

  • Participant understanding of heart disease differed significantly from expert models, impacting hypothesis formulation and interpretation.
  • Inaccurate or imprecise domain knowledge led to searches for irrelevant information and confirmation bias.
  • Online search skills improved efficiency but did not resolve fundamental information-seeking difficulties.

Conclusions:

  • Lay individuals face persistent health information search challenges, often linked to their domain knowledge and hypothesis evaluation.
  • Web experience and general search skills do not fully mitigate these difficulties.
  • Informatics solutions, including improved portals, websites, and educational tools, are needed to support users.