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

Decision Making: P-value Method01:09

Decision Making: P-value Method

5.4K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
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...
5.4K
Bonferroni Test01:10

Bonferroni Test

2.7K
The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
2.7K
P-value01:10

P-value

6.9K
P-value is one of the most crucial concepts in statistics.
P-value stands for the probability value.  P-value is the probability that, if the null hypothesis is true, the results from another randomly selected sample will be as extreme or more extreme as the results obtained from the given sample.
A large P-value calculated from the data indicates to  not reject the null hypothesis. But a higher P-value does not mean that the null hypothesis is true. The smaller the P-value, the more...
6.9K
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

251
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...
251
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

4.0K
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...
4.0K
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

3.3K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
3.3K

You might also read

Related Articles

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

Sort by
Same author

Editorial for the Special Collection "MCP 2022".

Biometrical journal. Biometrische Zeitschrift·2025
Same author

Multiple multi-sample testing under arbitrary covariance dependency.

Statistics in medicine·2023
Same author

Long-term temporal evolution of extreme temperature in a warming Earth.

PloS one·2023
Same author

Multiple two-sample testing under arbitrary covariance dependency with an application in imaging mass spectrometry.

Biometrical journal. Biometrische Zeitschrift·2022
Same author

Special issue on multiple comparisons (MCP 2019).

Biometrical journal. Biometrische Zeitschrift·2022
Same author

Obstructive sleep apnea syndrome as a rare presentation in a young girl with a central nervous system tumor.

Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine·2021
Same journal

Ensuring Quality in Preclinical Research: The Importance of Being Human.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Addressing Cluster-Level Treatment Effect Heterogeneity in Sample Size Determination for Hierarchical 2 × 2 Factorial Designs.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

A Multiple Imputation Approach to Distinguish Curative From Life-Prolonging Effects in the Presence of Missing Covariates.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Tests for Categorical Data Beyond Pearson: A Distance Covariance and Energy Distance Approach.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Nonparametric Estimation of the Patient-Weighted While-Alive Estimand.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Two-Stage Multiple Test Procedures Controlling False Discovery Rate With Auxiliary Variable and Their Application to Set4 <math><semantics><mi>Δ</mi> <annotation>$\Delta$</annotation></semantics></math> Mutant Data.

Biometrical journal. Biometrische Zeitschrift·2026
See all related articles

Related Experiment Video

Updated: Jul 12, 2025

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization
13:55

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization

Published on: February 3, 2013

18.5K

Multiple testing of composite null hypotheses for discrete data using randomized p-values.

Daniel Ochieng1, Anh-Tuan Hoang1, Thorsten Dickhaus1

  • 1Institute for Statistics, University of Bremen, Bremen, Germany.

Biometrical Journal. Biometrische Zeitschrift
|October 19, 2023
PubMed
Summary
This summary is machine-generated.

Randomized p-values offer a solution to conservativeness in statistical testing, particularly with discrete test statistics or non-least favorable parameter configurations. These novel methods maintain validity and improve power under the alternative hypothesis.

Keywords:
conservative testsdiscretely distributed test statisticsgroup testingmultiple comparisonsrandomized tests

More Related Videos

How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study
05:33

How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study

Published on: September 8, 2021

6.6K
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

42.1K

Related Experiment Videos

Last Updated: Jul 12, 2025

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization
13:55

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization

Published on: February 3, 2013

18.5K
How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study
05:33

How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study

Published on: September 8, 2021

6.6K
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

42.1K

Area of Science:

  • Statistics
  • Hypothesis Testing

Background:

  • P-values from continuous test statistics are typically uniform under the null hypothesis.
  • Conservativeness in p-values arises from discrete test statistics or non-least favorable parameter configurations (LFCs).

Purpose of the Study:

  • To introduce and evaluate two novel approaches using randomized p-values.
  • To address conservativeness in hypothesis testing for discrete statistics and composite nulls.

Main Methods:

  • Development of two randomized p-value approaches.
  • Application to composite null hypothesis testing under binomial and group testing models.
  • Validation of randomized p-values for discrete statistical models within exponential families.

Main Results:

  • Proposed randomized p-values are less conservative under the null hypothesis compared to nonrandomized versions.
  • Randomized p-values are stochastically not smaller under the alternative hypothesis, indicating maintained or improved power.
  • Demonstrated validity of randomized p-values in various discrete statistical models.

Conclusions:

  • Randomized p-values effectively mitigate conservativeness in hypothesis testing.
  • The proposed methods are valid and offer advantages in statistical power.
  • These approaches are applicable to complex statistical models and designs.