Related Experiment Video
Updated: Jul 14, 2026

Sampling Soils in a Heterogeneous Research Plot
Published on: January 7, 2019
Optimisation of the T-square sampling method to estimate population sizes
Kristof Bostoen1, Zaid Chalabi, Rebecca F Grais
1Department of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK. Kristof.Bostoen@lshtm.ac.uk
Abstract:
Population size and density estimates are needed to plan resource requirements and plan health related interventions. Sampling frames are not always available necessitating surveys using non-standard household sampling methods. These surveys are time-consuming, difficult to validate, and their implementation could be optimised. Here, we discuss an example of an optimisation procedure for rapid population estimation using T-Square sampling which has been used recently to estimate population sizes in emergencies. A two-stage process was proposed to optimise the T-Square method wherein the first stage optimises the sample size and the second stage optimises the pathway connecting the sampling points. The proposed procedure yields an optimal solution if the distribution of households is described by a spatially homogeneous Poisson process and can be sub-optimal otherwise. This research provides the first step in exploring how optimisation techniques could be applied to survey designs thereby providing more timely and accurate information for planning interventions.
Related Concept Videos
Distributions to Estimate Population Parameter
Choosing Between z and t Distribution
Estimating Population Mean with Unknown Standard Deviation
William S. Gosset (1876–1937) of the Guinness...
Student t Distribution
The Student t distribution was developed by William S. Goset (1876–1937) of the...
Sample Size Calculation
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
Testing a Claim about Mean: Unknown Population SD
Estimating a population mean requires the samples to be approximately normally distributed. The data should be collected from the randomly selected samples having no sampling bias. There is no specific requirement for sample size. But if the sample size is less than 30, and we don't know the population standard deviation, a different approach is used; instead...

