Bootstrap and posterior probabilities measure confidence in the branches of evolutionary trees.
Bootstrap support above 70 to 95 percent is considered good; Bayesian posterior probabilities tend to be higher. Model selection by AIC or BIC. Topology tests compare alternative trees.
Bootstrap values are not probabilities that clades are true. Long-branch attraction can mislead. Use multiple methods.
Key idea: support values summarise confidence.
Bootstrap and posterior probabilities measure confidence in the branches of evolutionary trees.
Phylogenetic Statistics is one of the 51 topics in Lab Methods & Biostatistics. It is rated Advanced, so it suits learners who want specialist depth. Related topics in this area include Bayesian Statistics, P-Values and Multiple Testing, Null Hypothesis.
SH test compares tree likelihoods.
Work through the Beginner topics in Lab Methods & Biostatistics first, such as Scientific Method, Hypothesis, Variables, then return to Phylogenetic Statistics. Advanced topics assume the Intermediate vocabulary.
The closest topics to Phylogenetic Statistics are Bayesian Statistics, P-Values and Multiple Testing, Null Hypothesis, Chi-Squared Test. Comparing them side by side is a good way to see how the ideas connect.
Read the summary and the in-depth section, then use the quick check on the interactive lesson for Phylogenetic Statistics. To practise the whole of Lab Methods & Biostatistics, use the Lab Methods & Biostatistics quiz.
Phylogenetic Statistics: Bootstrap and posterior probabilities measure confidence in the branches of evolutionary trees. Bayesian Statistics: Updates probabilities for hypotheses using prior knowledge and observed data. Read both side by side and note what each one explains.
First, read the summary and write the definition in your own words. Second, read the in-depth section and note any example. Third, compare it with Bayesian Statistics and explain the difference aloud. Finally, take the Lab Methods & Biostatistics quiz and come back to this page for any question you miss.
Updates probabilities for hypotheses using prior knowledge and observed data.
📊 Lab Methods & BiostatisticsP-values give the probability of data under the null; corrections like Bonferroni control false positives across many tests.
📊 Lab Methods & BiostatisticsThe default assumption of no effect or difference, which data may lead us to reject.
📊 Lab Methods & BiostatisticsCompares observed and expected counts, for example testing Mendelian ratios.
📊 Lab Methods & Biostatistics