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

Randomized Experiments01:13

Randomized Experiments

8.8K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
8.8K
Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

908
The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
908
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

498
The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and 0s. In...
498
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

6.0K
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...
6.0K
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

5.0K
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...
5.0K
Random Sampling Method01:09

Random Sampling Method

14.0K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
14.0K

You might also read

Related Articles

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

Sort by
Same author

Joint Associations of Prenatal Per- and Polyfluoroalkyl Substances and Metal Mixtures with Adiposity in Childhood and Adolescence.

Environmental science & technology·2026
Same author

Streetview greenspace types and dementia risk in older adults: The Multi-Ethnic Study of Atherosclerosis.

American journal of epidemiology·2026
Same author

Leveraging Artificial Intelligence in Allergy, Asthma, and Immunology With Environmental Exposures.

Allergy·2026
Same author

Association of Short-Term Pollen Exposure With Lung Function in COPD Patients.

Chronic obstructive pulmonary diseases (Miami, Fla.)·2026
Same author

Large-scale antibody reactome profiling identifies herpesvirus-autoantigen associations underlying chronic diseases.

Research square·2026
Same author

Measurement error-robust causal inference via constructed instrumental variables.

Biometrics·2026
Same journal

Applying Bayesian Multivariable Mendelian Randomisation to Prioritise Candidate Causal Traits From High-Dimensional Data: Illustration From Estimation of the Effect of Maternal Metabolites on Offspring Birthweight.

Genetic epidemiology·2026
Same journal

Individualized Bayesian Inference Identifies Novel Genetic Variants for Parkinson's Disease.

Genetic epidemiology·2026
Same journal

DRIVE v3: Command Line Application for Identity-by-Descent Haplotype Clustering in Large Biobank Scale Data.

Genetic epidemiology·2026
Same journal

Deep Unsupervised Domain Adaptation for Translating Cancer Dependency Maps From Cell Lines to Breast Cancer Tumor Genomics.

Genetic epidemiology·2026
Same journal

Polygenic Risk Scores for Incident Dementia in the Multi-Ethnic Study of Atherosclerosis.

Genetic epidemiology·2026
Same journal

Outcome and Exposure Polygenic Risk Scores Can Help Reduce Information Bias and Selection Bias in Regression Estimates From Biobank Data.

Genetic epidemiology·2026
See all related articles

Related Experiment Video

Updated: Jan 4, 2026

Operant Procedures for Assessing Behavioral Flexibility in Rats
08:30

Operant Procedures for Assessing Behavioral Flexibility in Rats

Published on: February 15, 2015

21.5K

A flexible and nearly optimal sequential testing approach to randomized testing: QUICK-STOP.

Julian Hecker1,2, Ingo Ruczinski3, Michael H Cho2

  • 1Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.

Genetic Epidemiology
|November 13, 2019
PubMed
Summary
This summary is machine-generated.

We developed QUICK-STOP, a novel stopping rule for randomized hypothesis testing. This method significantly reduces computational burden in life science data analysis, outperforming existing approaches by tenfold.

Keywords:
association p-valuenext-generation sequencingpermutationrandomized testsequential testing

More Related Videos

Barnes Maze Testing Strategies with Small and Large Rodent Models
12:59

Barnes Maze Testing Strategies with Small and Large Rodent Models

Published on: February 26, 2014

43.3K
Testing for Metacognitive Responding Using an Odor-based Delayed Match-to-Sample Test in Rats
08:06

Testing for Metacognitive Responding Using an Odor-based Delayed Match-to-Sample Test in Rats

Published on: June 18, 2018

7.6K

Related Experiment Videos

Last Updated: Jan 4, 2026

Operant Procedures for Assessing Behavioral Flexibility in Rats
08:30

Operant Procedures for Assessing Behavioral Flexibility in Rats

Published on: February 15, 2015

21.5K
Barnes Maze Testing Strategies with Small and Large Rodent Models
12:59

Barnes Maze Testing Strategies with Small and Large Rodent Models

Published on: February 26, 2014

43.3K
Testing for Metacognitive Responding Using an Odor-based Delayed Match-to-Sample Test in Rats
08:06

Testing for Metacognitive Responding Using an Odor-based Delayed Match-to-Sample Test in Rats

Published on: June 18, 2018

7.6K

Area of Science:

  • Statistics
  • Bioinformatics
  • Genomics

Background:

  • Asymptotic theory in hypothesis testing can be problematic for life science datasets.
  • Permutation/simulation-based tests offer solutions but are computationally intensive, especially with small significance levels.
  • Stopping rules are crucial for efficient significance assessment while controlling error probabilities.

Purpose of the Study:

  • To introduce QUICK-STOP, a general stopping rule for randomized tests.
  • To provide a computationally efficient method for hypothesis testing in large datasets.
  • To rigorously control error probabilities and minimize computational burden.

Main Methods:

  • Derivation of a general stopping rule, QUICK-STOP, based on sequential testing theory.
  • Simulation studies to compare QUICK-STOP with existing stopping approaches.
  • Application of QUICK-STOP to a whole-genome sequencing study for lung function.

Main Results:

  • QUICK-STOP is easy to implement and rigorously controls error probabilities.
  • The method is nearly optimal in terms of expected draws.
  • QUICK-STOP outperforms current stopping approaches by a factor of 10 without additional computational cost.
  • Demonstrated utility in a single-variant analysis of whole-genome sequencing data.

Conclusions:

  • QUICK-STOP offers a significant advancement in efficient hypothesis testing for large-scale life science data.
  • The rule effectively addresses the computational challenges of randomized tests.
  • QUICK-STOP provides a rigorous and efficient solution for genomic data analysis.