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Related Concept Videos

The Evidence for Evolution02:55

The Evidence for Evolution

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Genetic variations accumulating within populations over generations give rise to biological evolution. Evolutionary changes can result in the formation of novel varieties and entire new species. These changes are responsible for the diverse forms of life inhabiting the planet. The evidence for evolution suggests that all living organisms descended from common ancestors.
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Confirmation Biases01:31

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The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
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Significance Testing: Overview01:04

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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...
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Null and Alternative Hypotheses01:16

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The actual hypothesis testing begins by considering two hypotheses. They are termed  the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints.
The null hypothesis, denoted by H0 is a statement of no difference between the variables—they are not related. This can often be considered the status quo. As  a result if you cannot accept the null, it requires some action.
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Statistical Significance01:50

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Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
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Hypothesis: Accept or Fail to Reject?01:17

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The outcome of any hypothesis testing leads to rejecting or not rejecting the null hypothesis. This decision is taken based on the analysis of the data, an appropriate test statistic, an appropriate confidence level, the critical values, and P-values. However, when the evidence suggests that the null hypothesis cannot be rejected, is it right to say, 'Accept' the null hypothesis?
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Related Experiment Video

Updated: Oct 10, 2025

Dissociation of the Confounding Influences of Expectancy and Integrative Difficulty Residing in Anomalous Sentences in Event-related Potential Studies
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Strongest Evidence Revisited.

Benjamin A Salisbury1

  • 1Department of Ecology and Evolution, Yale University, P.O. Box 208106, New Haven, Connecticut, 06520-8106.

Cladistics : the International Journal of the Willi Hennig Society
|December 14, 2021
PubMed
Summary

The strongest evidence (SE) phylogenetic method shows good performance in simulations, though parsimony is often better. SE is more accurate in specific scenarios, and jackknifing can improve both methods.

Area of Science:

  • Phylogenetic analysis
  • Evolutionary biology
  • Computational biology

Background:

  • The strongest evidence (SE) approach is a method for phylogenetic analysis.
  • Farris (2000) previously commented on the SE approach.
  • Evaluating the merits of the SE approach is crucial for phylogenetic inference.

Purpose of the Study:

  • To examine the foundational concepts of the strongest evidence (SE) approach.
  • To reevaluate the merits of the SE approach in phylogenetic analysis.
  • To compare the performance of SE with parsimony methods.

Main Methods:

  • Conceptual examination of the SE approach's null model.
  • Simulation testing of SE and parsimony methods.
  • Evaluation of jackknifing and iterative fixation of splits.

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Main Results:

  • SE's null model serves as a tree-specific reference for phylogenetic signal.
  • SE methods perform reasonably well in simulations.
  • Parsimony is generally more accurate and less biased than SE, but SE excels in certain circumstances.
  • Jackknifing benefits both SE and parsimony analyses.
  • Iterative fixation of splits shows potential as an auxiliary procedure.

Conclusions:

  • The strongest evidence (SE) approach is a viable method for phylogenetic analysis, with specific strengths.
  • Parsimony methods often outperform SE, but SE offers advantages in particular datasets.
  • Jackknifing and iterative fixation of splits are valuable adjuncts to phylogenetic analyses.