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Related Concept Videos

Bonferroni Test01:10

Bonferroni Test

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...
Accuracy and Errors in Hypothesis Testing01:13

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 Method01:14

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...
Multiple Comparison Tests01:13

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
Significance Testing: Overview01:04

Significance Testing: Overview

Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...
Statistical Hypothesis Testing01:16

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...

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When to Adjust for Multiple Testing: A Unifying Guiding Principle.

Sabine Hoffmann1, Simon Lemster2, Gary Collins3

  • 1Department of Statistics, LMU Munich, Munich, Germany.

Biometrical Journal. Biometrische Zeitschrift
|July 2, 2026
PubMed
Summary
This summary is machine-generated.

Researchers should adjust for multiple statistical tests only when emphasizing results due to small p-values. This principle clarifies when to apply corrections, enhancing medical research credibility.

Keywords:
Type I errorfamily‐wise error rategood practiceguidancehypothesis testingmultiplicityreporting

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Area of Science:

  • Biostatistics
  • Medical Research Methodology

Background:

  • Medical literature frequently reports multiple statistical tests.
  • Current guidance on adjusting for multiple testing is often unclear or contradictory.
  • This ambiguity can hinder analysis, encourage poor practices, and reduce research credibility.

Purpose of the Study:

  • To present a unifying principle for deciding when to adjust for multiple testing.
  • To clarify the application of multiple testing adjustments in complex scenarios.

Main Methods:

  • The study refines and illustrates a guiding principle for multiple testing adjustments.
  • The principle focuses on the emphasis placed on results with small p-values.
  • The approach is demonstrated in three complex multiple testing settings.

Main Results:

  • A clear principle is proposed: adjust for multiple testing if and only if results are emphasized due to small p-values.
  • This principle provides a framework for statisticians and researchers.
  • The principle aids in selecting appropriate adjustment strategies.

Conclusions:

  • The proposed principle offers clarity on multiple testing adjustments.
  • Consistent application can improve the rigor and interpretability of medical research.
  • This guidance aims to safeguard the credibility of published medical findings.