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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...
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Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

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One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
Bending of Material: Problem Solving01:09

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Related Experiment Video

Updated: Jul 17, 2026

A Coupled Experiment-finite Element Modeling Methodology for Assessing High Strain Rate Mechanical Response of Soft Biomaterials
11:28

A Coupled Experiment-finite Element Modeling Methodology for Assessing High Strain Rate Mechanical Response of Soft Biomaterials

Published on: May 18, 2015

Determination of suitable sample sizes for multi-patient based finite element studies.

I A J Radcliffe1, P Prescott, H S Man

  • 1Bioengineering Science Research Group, University of Southampton, Highfield, Southampton SO17 1BJ, UK.

Medical Engineering & Physics
|January 16, 2007
PubMed
Summary

Determining the optimal number of femurs for finite element analysis is crucial for accounting for patient variation. This study provides sample sizing methods to ensure accurate assessments of joint replacements and implant designs.

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Area of Science:

  • Biomechanics and Biomaterials
  • Orthopaedic Engineering
  • Computational Mechanics

Background:

  • Finite element analysis (FEA) is widely used for joint replacement assessment.
  • Existing FEA studies often use single samples, neglecting inter-patient variability in bone geometry and material properties.
  • Advances in CT-based analysis enable multi-sample studies, necessitating sample size determination.

Purpose of the Study:

  • To investigate factors for determining the required sample size in FEA of joint replacements.
  • To explore sample sizing techniques for comparing strain distribution (intact vs. implanted femur) and multiple implant designs.
  • To provide guidance on sample size for statistically significant comparisons between implant designs.

Main Methods:

  • Utilized sample sizing calculations based on achieving desired result precision.
  • Employed sample sizing calculations for detecting significant differences between two designs.
  • Conducted an example analysis on femoral head resurfacing to demonstrate sample size effects.

Main Results:

  • Demonstrated that a group of femurs can achieve reasonable statistical precision in FEA.
  • Determined suitable sample sizes for analyzing statistically significant differences between groups of femurs with varying design parameters.
  • Highlighted the importance of sample size calculations for accurate comparative analyses.

Conclusions:

  • Sample size determination is recommended for accurate FEA of joint replacements.
  • Statistical precision and the ability to detect significant differences can be achieved with appropriate sample sizes.
  • Practical considerations must be balanced with the need for adequate sample sizes in FEA studies.