"a random subsample of" is a correct and usable phrase in written English.
It can be used to refer to a portion or subset of a larger group or sample that has been chosen randomly. Example: The researchers selected a random subsample of 100 participants from the original pool of 500 individuals for their study.
And that calculation is based on just one type of error, that which is unavoidable from taking a random subsample of a larger population.
Like other polls, the Current Population Survey is subject to sampling error, a result of collecting data among a random subsample of the population rather than everyone in the state.
The population size is 110, and the dots represent a random subsample of the population to show the variation.
We focus our analysis on a random subsample (of 1,000 recipes) of the five cuisines in the original dataset that contain more than 1,000 recipes.
These permutations include bagging procedures [50], [51] where a random subsample of the original dataset is taken to construct regression trees.
Data were collected from a random subsample of 725 women using structured case record form (CRF) interviews, coital diaries (CD) and in-depth interviews (IDI).
Our case-cohort study was based on two prospective cohorts from the relatively homogeneous Finnish population, the sub-cohort representing a random subsample of the original cohort.
We inspected fish collections from the Chinese museums and identified a random subsample of approximately 10 specimens per species to assure correct species assignments.
Data were collected from a random subsample of 725 women (7.7% of the trial population) using structured interviews, coital diaries, in-depth interviews, counting returned gel applicators, focus group discussions, and ethnography.
In the first phase random points were chosen on aerial photographs and classified by land cover and forest type, and in the second a random subsample of each class were selected and established as field plots.
Random Forests uses an iterative Bootstrap with replacement (64% of data per Bootstrap replicate) to construct an ensemble of "weak learners" (CARTs based on a random subsample of data).
