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Related Concept Videos

Types of Hypothesis Testing01:11

Types of Hypothesis Testing

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There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
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...
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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.
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Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
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Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
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Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
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,...
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Monitoring Gut Acidification in the Adult Drosophila Intestine
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Generality in multispecies responses to ocean acidification revealed through multiple hypothesis testing.

Allison K Barner1, Francis Chan1, Annaliese Hettinger1,2

  • 1Department of Integrative Biology, Oregon State University, Corvallis, Oregon.

Global Change Biology
|July 27, 2018
PubMed
Summary

Ocean acidification impacts marine life, but species responses vary. This study found that larger coralline algae, regardless of species, exhibit higher calcification and photosynthesis rates under these changing ocean conditions.

Keywords:
allometrycalcificationcoralline algaeeco-physiologyfunctional groupsfunctional traitsglobal changerocky intertidal

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Area of Science:

  • Marine Biology
  • Oceanography
  • Ecology

Background:

  • Ocean acidification poses a significant threat to marine calcifying species.
  • Species-specific responses to ocean acidification are not fully understood, hindering predictions of ecosystem impacts.

Purpose of the Study:

  • To investigate the drivers of physiological performance in coralline algae under ocean acidification.
  • To determine the relative importance of various factors influencing calcification and photosynthesis rates in these algae.

Main Methods:

  • Examined five species of turf-forming coralline algae.
  • Quantified physiological performance (calcification, photosynthesis) under simulated ocean acidification.
  • Tested ten alternative hypotheses to identify key drivers of variation in performance.

Main Results:

  • Allometric traits, specifically noncalcified mass (size), were the strongest predictors of calcification and photosynthesis rates.
  • Larger individuals consistently showed higher rates of calcification and photosynthesis across tested conditions.
  • Species identity, evolutionary relatedness, habitat, shape, and structural composition did not significantly explain performance variation.

Conclusions:

  • Species size is a critical, generalizable factor influencing coralline algae performance under ocean acidification.
  • Focusing on a single species or limited hypotheses can lead to underestimation of general physiological responses.
  • Understanding common drivers across taxa is essential for predicting the impacts of global change on marine ecosystems.