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Related Concept Videos

Bootstrapping01:24

Bootstrapping

The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is small or...
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...
Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

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.
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

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).
Hypothesis Test for Test of Independence01:16

Hypothesis Test for Test of Independence

The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
H0: The two variables (factors)...
Goodness-of-Fit Test01:16

Goodness-of-Fit Test

The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...

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Related Experiment Video

Updated: Jun 16, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

First-order correct bootstrap support adjustments for splits that allow hypothesis testing when using maximum

Edward Susko1

  • 1Department of Mathematics and Statistics, Dalhousie University, Halifax, Nova Scotia, Canada. susko@mathstat.dal.ca

Molecular Biology and Evolution
|February 16, 2010
PubMed
Summary
This summary is machine-generated.

Bootstrap support values, a common measure of phylogenetic uncertainty, are adjusted for hypothesis testing in maximum likelihood phylogenetics. New methods provide statistically interpretable P-value-like measures for phylogenetic splits.

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Area of Science:

  • Phylogenetics
  • Computational Biology
  • Statistical Modeling

Background:

  • Bootstrap support is the most frequent measure of phylogenetic uncertainty for splits.
  • Interpreting bootstrap support values for hypothesis testing has been challenging.
  • Previous research indicates bootstrap support is not first-order correct for hypothesis testing.

Purpose of the Study:

  • To develop methods for adjusting bootstrap support values in a maximum likelihood (ML) setting.
  • To provide adjusted bootstrap support values with an interpretation analogous to P values.
  • To address data-dependent hypotheses about phylogenetic splits.

Main Methods:

  • Developed methods to adjust bootstrap support values within a maximum likelihood framework.
  • Utilized examples and simulation settings to evaluate adjustment methods.
  • Considered adjustments for data-dependent hypotheses, where splits are identified via ML estimation.

Main Results:

  • Adjusted bootstrap support values provide a P-value-like interpretation, e.g., >95% adjusted support occurs 5% of the time when a split is absent.
  • Adjustments generally increase the level of support.
  • The nature of the adjustment is relatively consistent across parameter settings.
  • For data-dependent hypotheses, bootstrap probability often requires downward adjustment.

Conclusions:

  • Adjusted bootstrap support offers a statistically rigorous interpretation for phylogenetic splits.
  • The developed methods enhance the reliability of phylogenetic inference.
  • Accounting for data-dependency is crucial for accurate hypothesis testing in phylogenetics.