Skip to main content
Home
Multiple Testing Correction
A tool for life science researchers for multiple hypothesis testing correction

What is multipletesting.com is useful for?

Scientists from nearly all disciplines face the problem of simultaneously evaluating many hypotheses. Conducting multiple comparisons increases the likelihood that a significant proportion of associations will be false positives, clouding real discoveries. Several strategies exist to overcome the problem of multiple hypothesis testing. In our paper we provide a step by step description of each multiple testing correction method with clear examples and present an easyto follow guide for selecting the most suitable correction technique. Our multiple testing correction tool provides the five most frequently used adjustment tools, including the Bonferroni, the Holm (step-down), the Hochberg (step-up) corrections, and allows to calculate the False Discovery Rate (FDR) and q-values.

Multiple Testing Correction

Start


perform multiple hypothesis testing using a list of p values

Publication


read our guide to multiple hypothesis testing

Footer menu

MultipleTesting.com has been utilized among others in studies published in:

Multipletesting.com cited by papers

Try our other web tools as well:

Validate predictive biomarkers Validate predictive biomarkers Mutation vs gene expression Mutation vs gene expression Kaplan-Meier plotter Kaplan-Meier plotter Compare normal and tumor Compare normal and tumor

The more you look, the more you see…

Unfortunately, the multiplicity issue is still common in life sciences, economics, and material sciences. The common scenarios include when one fits a multiple regression model and wishes to decide which coefficients are different from zero. The opportunity of multiplicity can also be substantial when researchers try to salvage a negative study. For example, when a primary endpoint does not show statistical significance for a treatment, the investigators often try to analyze different endpoints among subsets of subjects with different statistical tests. Because each statistical test has the potential to introduce error, the increased number of comparisons will produce associations simply by chance. Massive-scale experiments involving high throughput data also create opportunities for spurious discoveries. Such studies may include identifying genes that are differentially expressed based on microarray or RNA-Seq experiments, evaluating the results of genome- wide association studies, or searching a protein database for homologs. In a nutshell, the more tests we perform, the more likely we will get a false-positive result.
Show — Footer menu Hide — Footer menu
  • Home
  • Contact
  • Log in
  • Copyright: Research Centre for Natural Sciences (2021-2022)