Related Experiment Video
Updated: Feb 2, 2026

A Standardized Protocol for Preference Testing to Assess Fish Welfare
Published on: February 22, 2020
Phylogenomic incongruence, hypothesis testing, and taxonomic sampling: The monophyly of characiform fishes
Ricardo Betancur-R1,2,3, Dahiana Arcila2,3,4, Richard P Vari4
1Department of Biology, University of Puerto Rico, Río Piedras Campus, San Juan, Puerto Rico, 00931.
Abstract:
Phylogenomic studies using genome-wide datasets are quickly becoming the state of the art for systematics and comparative studies, but in many cases, they result in strongly supported incongruent results. The extent to which this conflict is real depends on different sources of error potentially affecting big datasets (assembly, stochastic, and systematic error). Here, we apply a recently developed methodology (GGI or gene genealogy interrogation) and data curation to new and published datasets with more than 1000 exons, 500 ultraconserved element (UCE) loci, and transcriptomic sequences that support incongruent hypotheses. The contentious non-monophyly of the order Characiformes proposed by two studies is shown to be a spurious outcome induced by sample contamination in the transcriptomic dataset and an ambiguous result due to poor taxonomic sampling in the UCE dataset. By exploring the effects of number of taxa and loci used for analysis, we show that the power of GGI to discriminate among competing hypotheses is diminished by limited taxonomic sampling, but not equally sensitive to gene sampling. Taken together, our results reinforce the notion that merely increasing the number of genetic loci for a few representative taxa is not a robust strategy to advance phylogenetic knowledge of recalcitrant groups. We leverage the expanded exon capture dataset generated here for Characiformes (206 species in 23 out of 24 families) to produce a comprehensive phylogeny and a revised classification of the order.
Related Concept Videos
Types of Hypothesis Testing
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p...
Errors In Hypothesis Tests
Statistical Hypothesis Testing
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
Accuracy and Errors in Hypothesis Testing
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%...
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
Osmoregulation in Fishes

