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Sample Size Calculation

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Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
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Related Experiment Video

Updated: Jun 23, 2026

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems

Published on: July 30, 2019

Sufficient sampling for asymptotic minimum species richness estimators.

Anne Chao¹, Robert K Colwell, Chih-Wei Lin

¹Institute of Statistics, National Tsing Hua University, Hsin-Chu, 30043 Taiwan.

|May 20, 2009

Summary

This study introduces a new statistical method to estimate the minimum sampling effort needed to detect all species in biodiversity surveys. The method helps researchers determine how much more sampling is required to reach estimated species richness.

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Last Updated: Jun 23, 2026

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems

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Published on: July 30, 2019

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools

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Published on: November 20, 2017

Area of Science:

Ecology
Conservation Biology
Statistical Ecology

Background:

Biodiversity surveys are often labor-intensive and fail to detect rare species.
Existing statistical methods estimate total species richness but not the sampling effort needed to detect them.

Purpose of the Study:

To develop a statistically rigorous nonparametric method for estimating the minimum additional sampling effort required to detect a target proportion of species richness.

Main Methods:

The method utilizes Chao1 and Chao2 nonparametric estimators based on rare species frequencies.
Performance was evaluated using simulations from large biodiversity inventories (Lepidoptera, BCI woody plants).
The method was applied to seven published biodiversity datasets.

Main Results:

The method accurately estimates necessary sampling effort, performing well even with spatial aggregation.
Detecting all estimated species requires 1.05 to 10.67 times the original sampling effort (median ~2.23).
Detecting 90% of species requires substantially less effort (0.33-1.10 times original effort, median ~0.80).

Conclusions:

The developed method provides a robust tool for planning biodiversity surveys.
It quantifies sampling effort needed to achieve specific species detection targets.
An Excel tool is available for practical application in ecological research.