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Review and Preview

Review and Preview

In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
Percentiles are a type of fractile that partition data into...

Review and Preview

Review and Preview

Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...

Random and Systematic Errors

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...

Systematic Sampling Method

Systematic Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
Systematic sampling is one of the simplest methods...

Propagation of Uncertainty from Systematic Error

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...

Systematic Error: Methodological and Sampling Errors

Systematic Error: Methodological and Sampling Errors

In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...

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Related Experiment Video

Updated: Jan 26, 2026

Simulator Training for Endovascular Neurosurgery

Simulator Training for Endovascular Neurosurgery

Published on: May 6, 2020

Simulation training in vitreoretinal surgery: a systematic review.

Rasmus Christian Rasmussen¹, Jakob Grauslund^2,3, Anna Stage Vergmann^2,3

¹Department of Ophthalmology, Odense University Hospital, J.B. Winsløws Vej 4, DK-5000, Odense C, Denmark. Chrisrasmussen@live.dk.

BMC Ophthalmology

|April 13, 2019

Summary

Simulator-based training in vitreoretinal surgery (VRS) shows promise for skill acquisition. Further research is needed to confirm skill transfer from simulators to actual operating room procedures.

Keywords:

EYESI Simulation-based training Surgical simulator Vitreoretinal surgery

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Area of Science:

Ophthalmology
Medical Simulation
Surgical Training

Background:

Simulator-based training is increasingly utilized in surgical education.
Vitreoretinal surgery (VRS) presents unique challenges for trainees.
A systematic review is needed to assess the current state of VRS simulation.

Purpose of the Study:

To systematically review literature on simulator-based training in VRS.
To examine results of simulated VRS and their application in training.
To evaluate the quality of existing research on VRS simulators.

Main Methods:

Systematic literature search of PubMed, Embase, and Cochrane Library.
Inclusion of English-language articles on simulated VRS training.
Qualitative analysis of selected studies due to heterogeneity.

Main Results:

Seven studies met inclusion criteria from 203 articles.
Six studies utilized the EyeSi® Surgical simulator.
Positive performance curves and construct validity were reported in most studies.

Conclusions:

VRS simulators can assess and aid in acquiring surgical skills.
Evidence for skill transfer from simulator to operating room is limited.
Future research should focus on validation frameworks and standardized study designs.