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Related Concept Videos

Sample Size Calculation01:19

Sample Size Calculation

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.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
Bootstrapping01:24

Bootstrapping

The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is small or...
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate + error bound)
The...
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the Guinness...

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Related Experiment Videos

Sample size calculation for training ensemble machine learning models on health data.

Nicholas Mitsakakis1, Dan Liu1,2, Thomas Walters3

  • 1CHEO Research Institute, Ottawa, ON, Canada.

Patterns (New York, N.Y.)
|June 22, 2026
PubMed
Summary
This summary is machine-generated.

Researchers created a sample size calculator for machine learning (ML) models in health research. This tool helps determine adequate sample sizes for ensemble ML models, improving study design and data analysis.

Keywords:
sample size calculation

Related Experiment Videos

Area of Science:

  • Machine Learning
  • Health Research
  • Statistical Modeling

Background:

  • Health research studies frequently face limitations due to small sample sizes.
  • Training machine learning (ML) models necessitates substantial datasets, creating a gap in guidance for adequate sample size determination.
  • Existing literature lacks comprehensive methods for calculating sample sizes for ML model development.

Purpose of the Study:

  • To develop an empirically derived sample size calculator for ensemble ML models.
  • To predict the necessary sample size for achieving a specific level of prognostic performance with a defined probability.
  • To compare the accuracy of the developed calculator against common heuristics and statistical approaches.

Main Methods:

  • Developed an empirically derived sample size calculator for ensemble ML models, including random forests, light gradient boosting machine (LGBM), and extreme gradient boosting (XGBoost).
  • Defined prognostic performance as the sample area under the ROC curve (ROC-AUC) relative to the optimal model trained on the full dataset.
  • Compared the calculator's accuracy against three common heuristics and one statistical approach for sample size calculation.

Main Results:

  • The developed calculator demonstrated significantly better accuracy for tree-based ensemble ML models compared to other methods.
  • For instance, the median relative error in sample size prediction was 25% for achieving 85% of optimal performance with 90% certainty for LGBM.
  • The calculator effectively predicts sample sizes needed for desired prognostic performance levels.

Conclusions:

  • The new sample size calculator provides a more accurate method for determining adequate sample sizes in health research utilizing ML.
  • This tool addresses the critical need for sample size guidance in ML model development, particularly for tree-based ensemble methods.
  • Improved sample size estimation can enhance the reliability and generalizability of ML models in health research.