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Sample Size Calculation01:19

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.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Bootstrapping01:24

Bootstrapping

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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...
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Estimating Population Mean with Known Standard Deviation01:16

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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 +...
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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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...
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Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

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The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
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Related Experiment Video

Updated: May 25, 2025

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
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Estimating the Minimum Sample Size for Neural Network Model Fitting-A Monte Carlo Simulation Study.

Yongtian Cheng1, Konstantinos Vassilis Petrides1, Johnson Li2

  • 1Division of Psychology and Language Sciences, University College London (UCL), 26 Bedford Way, London WC1H 0AP, UK.

Behavioral Sciences (Basel, Switzerland)
|February 26, 2025
PubMed
Summary
This summary is machine-generated.

Neural networks (NNs) show unstable performance with ordinal psychological data. Researchers suggest avoiding NNs for ordinal independent variables, especially with nonlinear relationships, due to unreliable results.

Keywords:
neural networksordinal datasetpredictive performancereproducibilitysample size

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Area of Science:

  • Psychology
  • Computer Science
  • Machine Learning
  • Psychometrics

Background:

  • Machine learning (ML) methods, particularly neural networks (NNs), are increasingly used in psychological studies for supervised model fitting on psychometric data.
  • Psychometric independent variables are often ordinal and low-dimensional, posing unique challenges for ML model performance.
  • There is a lack of guidance on sample size planning for NN applications in psychology.

Purpose of the Study:

  • To investigate the performance of neural networks (NNs) with varying sample sizes using simulated psychometric data.
  • To determine appropriate minimum sample sizes for NN model fitting based on performance criteria.
  • To evaluate the impact of ordinal independent variables on NN performance in psychological research.

Main Methods:

  • A simulation study was conducted to assess NN performance across different sample sizes.
  • Simulations included both linear and nonlinear relationships between variables.
  • Performance criteria for minimum sample size were defined as 95% of models nearing theoretical maximum performance and 80% outperforming linear models.

Main Results:

  • Neural network performance was found to be unstable when using ordinal variables as independent predictors.
  • The study identified specific sample size recommendations based on the defined performance metrics.
  • Results indicate potential limitations of using NNs with ordinal data common in psychological research.

Conclusions:

  • Neural networks may not be suitable for psychometric data with ordinal independent variables, particularly when nonlinear relationships are present.
  • Researchers should exercise caution when applying NNs to such datasets.
  • Further research is needed to explore alternative ML methods or data preprocessing techniques for ordinal psychometric data.