Reading a methodology for reporting plant/seedling survival I found the following statement:

In small plantations or very small strata (1 to 3 hectares) the sampling error size is usually high and, therefore, a higher number of sampling sites is needed to obtain representative estimations (Spittler, 1995)

My question is, why is sampling error size high in smaller plantations/populations? I'm hoping for an answer with a real-world example (not mathematical definitions); I've found this to be in agreement with sample size estimates, where sample size is not linearly related to population (evident from the formulas). The rather unavailable/outdated reference is below.

SPITTLER, P. 1995. Guía Técnica para el Inventario Rápido de Bosques Secundarios en la Zona Norte de Costa Rica. COSEFORMA. Alajuela, Costa Rica.


This sounds to me like a version of the Central Limit Theorem. Essentially, if the plot you look at is small, there might only be a few plants, so the survival rate is going to be susceptible to large swings due to chance. At one extreme, say there were only 10 seedlings. A difference of two additional ones surviving just by chance could shift you from 4/10=40% survival rate to a 6/10=60% survival rate.

If you have a large plot with significantly more plants (say 1,000), you can be more confident in your results - i.e. a smaller error from the true value due to the fact that you're looking at a sample. There will still be variation between plots due to chance, but you're unlikely to result in variations like the 40% to 60% swing above.


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