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

Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

718
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%...
718
Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

4.4K
When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
4.4K
Hardy-Weinberg Principle01:49

Hardy-Weinberg Principle

62.6K
Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.
62.6K
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

1.7K
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
1.7K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

7.0K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
7.0K
Study Design in Statistics01:15

Study Design in Statistics

7.5K
A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research problem. Statistics is commonly for effective study design and more robust experiments,
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...
7.5K

You might also read

Related Articles

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

Sort by
Same author

Genetic investigation of non-affective psychosis and depression as causal risk factors for dementia.

BMJ mental health·2026
Same author

Co-expression-based models improve eQTL predictions for transcriptome-wide association studies and highlight new schizophrenia-associated genes.

Nature genetics·2026
Same author

Alzheimer's disease.

Lancet (London, England)·2026
Same author

Genetics to Improve Outcomes in Schizophrenia (GENios): A within-case molecular genetic study protocol.

PloS one·2026
Same author

Identification of common variants influencing risk of the three-repeat tauopathy Pick's disease: a genome wide association study.

medRxiv : the preprint server for health sciences·2026
Same author

Developing Topics.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025
Same journal

A Bayesian functional concurrent zero-inflated Dirichlet-multinomial regression model with application to infant microbiome.

Biostatistics (Oxford, England)·2026
Same journal

Towards optimal environmental policies: policy learning under arbitrary bipartite network interference.

Biostatistics (Oxford, England)·2026
Same journal

Multilevel functional quantile principal component analysis.

Biostatistics (Oxford, England)·2026
Same journal

Adaptive transfer learning for time-to-event modeling with applications in disease risk assessment.

Biostatistics (Oxford, England)·2026
Same journal

High-dimensional test for one-sided hypotheses.

Biostatistics (Oxford, England)·2026
Same journal

NBSR: a Negative Binomial Softmax Regression model for microRNA-seq data analysis.

Biostatistics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: May 6, 2026

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
10:17

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

23.3K

How allele frequency and study design affect association test statistics with misrepresentation errors.

Valentina Escott-Price1, Mansoureh Ghodsi, Karl Michael Schmidt

  • 1MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Cardiff CF14 4XN, UK.

Biostatistics (Oxford, England)
|November 2, 2013
PubMed
Summary
This summary is machine-generated.

Genotyping errors inflate false positive rates in genetic association tests, particularly with rare alleles. This study provides methods to quantify and correct for these errors, improving p-value accuracy.

Keywords:
Case–control studyGeneral association testGenomic controlGenotyping errorsNon-central χ2 distributionType-I error

More Related Videos

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

17.9K
Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

10.2K

Related Experiment Videos

Last Updated: May 6, 2026

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
10:17

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

23.3K
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

17.9K
Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

10.2K

Area of Science:

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Genotyping errors are a common issue in genetic studies.
  • These errors can impact the reliability of association test results.
  • Understanding the effect of errors is crucial for accurate genetic discoveries.

Purpose of the Study:

  • To evaluate the impact of genotyping errors on the type-I error rate of genetic association tests.
  • To develop methods for quantifying and correcting for genotyping errors.
  • To assess how population genotype frequencies influence the effect of errors.

Main Methods:

  • Derivation of formulae for scaling factors and non-centrality parameters under a symmetric allele-based genotyping error model.
  • Analysis of additive and recessive disease models.
  • Asymptotic analysis of the test statistic distribution in the presence of errors.

Main Results:

  • Genotyping errors lead to an increased false-positive rate that grows with sample size.
  • The effect of errors is strongly dependent on population genotype frequencies, with a pronounced impact on rare alleles.
  • Robustness against errors is observed with large minor allele frequencies.
  • Explicit formulae are provided for error quantification and p-value correction.

Conclusions:

  • Genotyping errors significantly compromise the accuracy of genetic association studies.
  • The developed methods allow for the correction of p-values to account for genotyping errors.
  • Awareness of allele frequency is critical when assessing the impact of genotyping errors.