Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Statistical Hypothesis Testing

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...

What is a Hypothesis?

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...

Null and Alternative Hypotheses

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...

Accuracy and Errors in Hypothesis Testing

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 Method

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...

Types of Hypothesis Testing

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.

You might also read

Related Articles

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

Sort by

Same author

A framework for assessing the impact of accelerated approval.

PloS one·2022

Same author

Bayesian screening for feature selection.

Journal of biopharmaceutical statistics·2022

Same author

BMA-Mod: A Bayesian model averaging strategy for determining dose-response relationships in the presence of model uncertainty.

Biometrical journal. Biometrische Zeitschrift·2018

Same author

Principled Structured Incorporation of Clinical Knowledge Into Strategic Development Decisions.

Therapeutic innovation & regulatory science·2018

Same author

Methods and Issues to Consider for Detection of Safety Signals From Spontaneous Reporting Databases: A Report of the DIA Bayesian Safety Signal Detection Working Group.

Therapeutic innovation & regulatory science·2018

Same author

Unified screening for potential elevated adverse event risk and other associations.

Statistics in medicine·2018

Same journal

A Drug-Environment Interaction Between PM<sub>2</sub> <sub>.5</sub> Concentration and Corticosteroid Use on Cardiovascular and Thromboembolic Events in Older Adults.

Pharmacoepidemiology and drug safety·2026

Same journal

Association Between Socioeconomic Conditions and Biologic Prescriptions for Inflammatory Bowel Diseases in the Brazilian Public Healthcare System: An Ecological Study.

Pharmacoepidemiology and drug safety·2026

Same journal

Effectiveness of Metformin in Preventing Colorectal Cancer Among Japanese Patients With Type 2 Diabetes: A Target Trial Emulation.

Pharmacoepidemiology and drug safety·2026

Same journal

Trends in Pharmacist-Prescribed Dispensing Records of HIV Pre-Exposure (2020-2025) and Post-Exposure Prophylaxis (2020-2024) in Brazil: A Time Series Analysis.

Pharmacoepidemiology and drug safety·2026

Same journal

French Consumption of Methylphenidate in Primary Care From 2016 to 2023, Impact of Prescribing Policy Changes-A Time-Series Analysis.

Pharmacoepidemiology and drug safety·2026

Same journal

Uptake and Use of Biologic Therapies in Paediatric Immune-Mediated Inflammatory Diseases: An Australian Population-Based Study.

Pharmacoepidemiology and drug safety·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 13, 2026

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

Generating and confirming hypotheses.

A Lawrence Gould¹

¹Merck Research Laboratories, North Wales, PA 19454, USA. goulda@merck.com

Pharmacoepidemiology and Drug Safety

|May 4, 2010

Summary

This summary is machine-generated.

Hypothesis testing requires careful consideration of data independence. Data used for hypothesis generation and testing from the same source are typically not truly independent, challenging Dr. Walker

More Related Videos

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

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

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

Published on: September 11, 2021

Related Experiment Videos

Last Updated: Jun 13, 2026

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

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

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

Published on: September 11, 2021

Area of Science:

Statistics
Data Science
Research Methodology

Background:

Dr. Walker proposed that hypotheses can be tested using the same data source that generated them, provided test data are independent of hypothesis-generating data.
This approach suggests splitting a dataset to form a hypothesis and then test it on separate, independent subsets from the same source.

Discussion:

This note critically examines the logical and statistical implications of Dr. Walker's assertion regarding data independence in hypothesis testing.
The core issue lies in the definition and practical application of 'independence' when both hypothesis generation and testing utilize data from a single, shared source.
It is argued that data subsets derived from the same source, even if partitioned, often fail to meet rigorous statistical independence criteria.

Key Insights:

The validity of testing hypotheses with data from the same source hinges on a precise understanding of statistical independence.
Data used for both hypothesis generation and subsequent testing, even when partitioned, may exhibit dependencies that compromise test integrity.
Careful consideration of the data-generating process is crucial to avoid biased results and ensure reliable scientific conclusions.

Outlook:

Further research is needed to establish robust methodologies for hypothesis testing when using partitioned data from a single source.
Developing clearer guidelines on what constitutes 'independent' data in such scenarios is essential for reproducible and valid scientific research.
The findings underscore the importance of adhering to strict statistical principles to maintain the integrity of research findings.