Related Experiment Video
Updated: May 15, 2026
Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease
Published on: August 9, 2024
Seeking health information on the web: positive hypothesis testing
1College of Business, University of South Florida St. Petersburg, Saint Petersburg, FL 33701, USA. vkayhan@usfsp.edu
Purpose:
The goal of this study is to investigate positive hypothesis testing among consumers of health information when they search the Web. After demonstrating the extent of positive hypothesis testing using Experiment 1, we conduct Experiment 2 to test the effectiveness of two debiasing techniques.
Methods:
A total of 60 undergraduate students searched a tightly controlled online database developed by the authors to test the validity of a hypothesis. The database had four abstracts that confirmed the hypothesis and three abstracts that disconfirmed it.
Results:
Findings of Experiment 1 showed that majority of participants (85%) exhibited positive hypothesis testing. In Experiment 2, we found that the recommendation technique was not effective in reducing positive hypothesis testing since none of the participants assigned to this server could retrieve disconfirming evidence. Experiment 2 also showed that the incorporation technique successfully reduced positive hypothesis testing since 75% of the participants could retrieve disconfirming evidence.
Conclusion:
Positive hypothesis testing on the Web is an understudied topic. More studies are needed to validate the effectiveness of the debiasing techniques discussed in this study and develop new techniques. Search engine developers should consider developing new options for users so that both confirming and disconfirming evidence can be presented in search results as users test hypotheses using search engines.
Related Concept Videos
01:16Statistical Hypothesis Testing
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
01:13Accuracy and Errors in Hypothesis Testing
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...
01:14Decision Making: Traditional Method
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...
01:09Decision Making: P-value Method
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
01:16Null and Alternative Hypotheses
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...
01:11Types of Hypothesis Testing
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.