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Pop-Inference: An educational application to evaluate statistical differences among populations.

Julio Arrontes¹

¹Departamento de Biología de Organismos y Sistemas University of Oviedo Oviedo Spain.

Ecology and Evolution

|June 26, 2018

Summary

Pop-Inference is an educational tool for teaching hypothesis testing in populations. It statistically compares demographic parameters using randomization tests and bootstrap confidence intervals, aiding in understanding population differences.

Keywords:

demography life table response experiments power projection matrix statistical inference

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Area of Science:

Ecology
Population Biology
Statistical Modeling

Background:

Hypothesis testing in population studies requires robust statistical tools.
Comparing demographic parameters across populations is crucial for ecological research.
Existing methods may lack flexibility for diverse demographic data types.

Purpose of the Study:

To introduce Pop-Inference, an educational application for teaching hypothesis testing with population data.
To provide a tool for statistically comparing demographic parameters among populations.
To facilitate understanding of randomization tests and bootstrap methods in population analysis.

Main Methods:

Utilizes projection matrices or raw demographic data as input.
Employs randomization tests to compare populations and assess demographic parameter differences.
Applies bootstrap methods for calculating confidence intervals of demographic parameters.
Includes global and pairwise tests, one-way life table response experiments (LTRE), and a priori comparisons.

Main Results:

Pop-Inference evaluates the hypothesis that demographic parameters differ significantly among populations.
The tool enables exploration of power analysis by simulating null and alternative hypotheses.
Investigates the relationship between statistical power and sample size while maintaining constant vital rates.

Conclusions:

Pop-Inference serves as a valuable educational resource for teaching advanced statistical concepts in population ecology.
The application enhances the ability to perform rigorous statistical comparisons of demographic data.
It supports a deeper understanding of hypothesis testing, power analysis, and sample size considerations in population studies.