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

Bonferroni Test01:10

Bonferroni Test

3.3K
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...
3.3K
Significance Testing: Overview01:04

Significance Testing: Overview

11.5K
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...
11.5K
Introduction to Nonparametric Statistics01:28

Introduction to Nonparametric Statistics

1.3K
Nonparametric statistics offer a powerful alternative to traditional parametric methods, useful when assumptions about the population distribution cannot be made. Unlike parametric tests, which require data to follow a specific distribution with well-defined parameters (such as the mean and standard deviation), nonparametric tests do not require such constraints. This makes them particularly valuable when dealing with small sample sizes, skewed data, or ordinal and categorical variables.
One of...
1.3K
Identifying Statistically Significant Differences: The F-Test01:14

Identifying Statistically Significant Differences: The F-Test

3.1K
The F-test is used to compare two sample variances to each other or compare the sample variance to the population variance. It is used to decide whether an indeterminate error can explain the difference in their values. The underlying assumptions that allow the use of the F-test include the data set or sets are normally distributed, and the data sets are independent of each other. The test statistic F is calculated by dividing one variance by another. In other words, the square of one standard...
3.1K
Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

383
The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in...
383
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

4.0K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
4.0K

You might also read

Related Articles

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

Sort by
Same author

Statistical errors undermine claims about the evolution of polysynthetic languages.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Still No Evidence for an Effect of the Proportion of Non-Native Speakers on Natural Language Complexity.

Entropy (Basel, Switzerland)·2024
Same author

Studying Lexical Dynamics and Language Change via Generalized Entropies: The Problem of Sample Size.

Entropy (Basel, Switzerland)·2020
Same author

Language structure is influenced by the number of speakers but seemingly not by the proportion of non-native speakers.

Royal Society open science·2019

Related Experiment Video

Updated: Jan 19, 2026

A New Approach for the Comparative Analysis of Multiprotein Complexes Based on 15N Metabolic Labeling and Quantitative Mass Spectrometry
08:04

A New Approach for the Comparative Analysis of Multiprotein Complexes Based on 15N Metabolic Labeling and Quantitative Mass Spectrometry

Published on: March 13, 2014

12.6K

A non-parametric significance test to compare corpora.

Alexander Koplenig1

  • 1Leibniz Institute for the German language (IDS), Mannheim, Germany.

Plos One
|September 20, 2019
PubMed
Summary

Classical significance tests are unsuitable for corpus linguistics due to unmet randomness assumptions. This paper introduces a new test to evaluate the significance of corpus linguistics findings, determining if results are substantial or due to chance.

Area of Science:

  • Corpus Linguistics
  • Statistical Methods

Background:

  • Classical null hypothesis significance tests (NHST) rely on randomness assumptions not met in corpus linguistics.
  • Existing methods lack a robust way to assess the relevance of observed linguistic patterns.

Purpose of the Study:

  • To propose a novel statistical test tailored for corpus linguistics.
  • To provide a method for judging the substantiality of linguistic results beyond chance variation.

Main Methods:

  • The paper outlines a new testing procedure designed for corpus data.
  • This method addresses the limitations of traditional significance testing in this field.

Main Results:

  • The proposed test allows researchers to determine if a linguistic attribute is pronounced enough to be considered significant.

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

7.4K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.3K

Related Experiment Videos

Last Updated: Jan 19, 2026

A New Approach for the Comparative Analysis of Multiprotein Complexes Based on 15N Metabolic Labeling and Quantitative Mass Spectrometry
08:04

A New Approach for the Comparative Analysis of Multiprotein Complexes Based on 15N Metabolic Labeling and Quantitative Mass Spectrometry

Published on: March 13, 2014

12.6K
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

7.4K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.3K
  • It offers a way to differentiate meaningful patterns from random fluctuations in corpora.

    • Conclusions:

    • The developed test is appropriate for corpus linguistics, overcoming the limitations of classical NHST.
    • This facilitates more reliable interpretation of differences and attributes within and between corpora.